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Gui_Beyond_Rule-Based_Agents_Active_Markov_Games_for_Realistic_Multi-Agent_Interaction_CVPR_2026_paper | Beyond Rule-Based Agents: Active Markov Games for Realistic Multi-Agent Interaction in Autonomous Driving | [
"Yuan Gui",
"Hongchen Luo",
"Jiao Wang",
"Liqi Qu"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Gui_Beyond_Rule-Based_Agents_Active_Markov_Games_for_Realistic_Multi-Agent_Interaction_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Gui_Beyond_Rule-Based_Agents_Active_Markov_Games_for_Realistic_Multi-Agent_Interaction_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Gui_Beyond_Rule-Based_Agents_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Gui_2026_CVPR,
author = {Gui, Yuan and Luo, Hongchen and Wang, Jiao and Qu, Liqi},
title = {Beyond Rule-Based Agents: Active Markov Games for Realistic Multi-Agent Interaction in Autonomous Driving},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Rec... | Current research in autonomous driving heavily relies on large-scale driving datasets for model fitting or trial-and-error learning strategies in simulation environments. However, these approaches suffer from limited behavioral diversity and fail to cover complex edge-case interactions. To address these limitations, we... |
Esfeh_Spectral_Conformal_Risk_Control_Distribution-Free_Tail_Guarantees_via_Bayesian_Quadrature_CVPR_2026_paper | Spectral Conformal Risk Control: Distribution-Free Tail Guarantees via Bayesian Quadrature | [
"Mohammad Mahdi Kazemi Esfeh",
"Qi Yan",
"Yongxing Zhang",
"Zahra Gholami",
"Renjie Liao",
"Purang Abolmaesumi"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Esfeh_Spectral_Conformal_Risk_Control_Distribution-Free_Tail_Guarantees_via_Bayesian_Quadrature_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Esfeh_Spectral_Conformal_Risk_Control_Distribution-Free_Tail_Guarantees_via_Bayesian_Quadrature_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Esfeh_Spectral_Conformal_Risk_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Esfeh_2026_CVPR,
author = {Esfeh, Mohammad Mahdi Kazemi and Yan, Qi and Zhang, Yongxing and Gholami, Zahra and Liao, Renjie and Abolmaesumi, Purang},
title = {Spectral Conformal Risk Control: Distribution-Free Tail Guarantees via Bayesian Quadrature},
booktitle = {Proceedings of the IE... | Modern vision systems are deployed in settings where occasional catastrophic failures matter more than average accuracy--for example in medical imaging, autonomous driving, and safety monitoring. While conformal prediction gives distribution-free uncertainty guarantees, most existing methods only control mean error and... |
Chen_VideoChat-M1_Collaborative_Policy_Planning_for_Video_Understanding_via_Multi-Agent_Reinforcement_CVPR_2026_paper | VideoChat-M1: Collaborative Policy Planning for Video Understanding via Multi-Agent Reinforcement Learning | [
"Boyu Chen",
"Zikang Wang",
"Zhengrong Yue",
"Kainan Yan",
"Chenyun Yu",
"Yi Huang",
"Zijun Liu",
"Yafei Wen",
"Xiaoxin Chen",
"Yang Liu",
"Peng Li",
"Yali Wang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Chen_VideoChat-M1_Collaborative_Policy_Planning_for_Video_Understanding_via_Multi-Agent_Reinforcement_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Chen_VideoChat-M1_Collaborative_Policy_Planning_for_Video_Understanding_via_Multi-Agent_Reinforcement_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Chen_VideoChat-M1_Collaborative_Policy_CVPR_2026_supplemental.pdf | 2511.19524 | cvf | @InProceedings{Chen_2026_CVPR,
author = {Chen, Boyu and Wang, Zikang and Yue, Zhengrong and Yan, Kainan and Yu, Chenyun and Huang, Yi and Liu, Zijun and Wen, Yafei and Chen, Xiaoxin and Liu, Yang and Li, Peng and Wang, Yali},
title = {VideoChat-M1: Collaborative Policy Planning for Video Understanding vi... | Most of the multi-agent video understanding frameworks adopt static and non-learnable tool invocation mechanisms, which limit the discovery of diverse clues essential for robust perception and reasoning regarding temporally or spatially complex videos. To address this challenge, we propose a novel Multi-agent system fo... |
Gu_Cycle-Consistent_Tuning_for_Layered_Image_Decomposition_CVPR_2026_paper | Cycle-Consistent Tuning for Layered Image Decomposition | [
"Zheng Gu",
"Min Lu",
"Zhida Sun",
"Dani Lischinski",
"Daniel Cohen-Or",
"Hui Huang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Gu_Cycle-Consistent_Tuning_for_Layered_Image_Decomposition_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Gu_Cycle-Consistent_Tuning_for_Layered_Image_Decomposition_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Gu_Cycle-Consistent_Tuning_for_CVPR_2026_supplemental.pdf | 2602.20989 | cvf | @InProceedings{Gu_2026_CVPR,
author = {Gu, Zheng and Lu, Min and Sun, Zhida and Lischinski, Dani and Cohen-Or, Daniel and Huang, Hui},
title = {Cycle-Consistent Tuning for Layered Image Decomposition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVP... | Disentangling visual layers in real-world images is a persistent challenge in vision and graphics, as such layers often involve non-linear and globally coupled interactions, including shading, reflection, and perspective distortion. In this work, we present an in-context image decomposition framework that leverages lar... |
Huang_SketchVL_Policy_Optimization_via_Fine-Grained_Credit_Assignment_for_Chart_Understanding_CVPR_2026_paper | SketchVL: Policy Optimization via Fine-Grained Credit Assignment for Chart Understanding and More | [
"Muye Huang",
"Lingling Zhang",
"Yifei Li",
"Yaqiang Wu",
"Jun Liu"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Huang_SketchVL_Policy_Optimization_via_Fine-Grained_Credit_Assignment_for_Chart_Understanding_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Huang_SketchVL_Policy_Optimization_via_Fine-Grained_Credit_Assignment_for_Chart_Understanding_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Huang_SketchVL_Policy_Optimization_CVPR_2026_supplemental.pdf | 2601.05688 | cvf | @InProceedings{Huang_2026_CVPR,
author = {Huang, Muye and Zhang, Lingling and Li, Yifei and Wu, Yaqiang and Liu, Jun},
title = {SketchVL: Policy Optimization via Fine-Grained Credit Assignment for Chart Understanding and More},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision an... | Charts are high-density visual carriers of complex data and medium for information extraction and analysis. Due to the need for precise and complex visual reasoning, automated chart understanding poses a significant challenge to existing Multimodal Large Language Models (MLLMs). Many MLLMs trained with reinforcement le... |
Chen_ArchSym_Detecting_3D-Grounded_Architectural_Symmetries_in_the_Wild_CVPR_2026_paper | ArchSym: Detecting 3D-Grounded Architectural Symmetries in the Wild | [
"Hanyu Chen",
"Ruojin Cai",
"Steve Marschner",
"Noah Snavely"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Chen_ArchSym_Detecting_3D-Grounded_Architectural_Symmetries_in_the_Wild_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Chen_ArchSym_Detecting_3D-Grounded_Architectural_Symmetries_in_the_Wild_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Chen_ArchSym_Detecting_3D-Grounded_CVPR_2026_supplemental.pdf | 2604.22202 | cvf | @InProceedings{Chen_2026_CVPR,
author = {Chen, Hanyu and Cai, Ruojin and Marschner, Steve and Snavely, Noah},
title = {ArchSym: Detecting 3D-Grounded Architectural Symmetries in the Wild},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mont... | Symmetry detection is a fundamental problem in computer vision, and symmetries serve as powerful priors for downstream tasks. However, existing learning-based methods for detecting 3D symmetries from single images have been almost exclusively trained and evaluated on object-centric or synthetic datasets, and thus fail ... |
He_Structural_Graph_Probing_of_Vision-Language_Models_CVPR_2026_paper | Structural Graph Probing of Vision-Language Models | [
"Haoyu He",
"Yue Zhuo",
"Yu Zheng",
"Qi R. Wang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/He_Structural_Graph_Probing_of_Vision-Language_Models_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/He_Structural_Graph_Probing_of_Vision-Language_Models_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/He_Structural_Graph_Probing_CVPR_2026_supplemental.pdf | 2603.27070 | cvf | @InProceedings{He_2026_CVPR,
author = {He, Haoyu and Zhuo, Yue and Zheng, Yu and Wang, Qi R.},
title = {Structural Graph Probing of Vision-Language Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = ... | Vision-language models (VLMs) achieve strong multimodal performance, yet how computation is organized across populations of neurons remains poorly understood. In this work, we study VLMs through the lens of neural topology, representing each layer as a within-layer correlation graph derived from neuron-neuron co-activa... |
Zhao_P-Flow_Prompting_Visual_Effects_Generation_CVPR_2026_paper | P-Flow: Prompting Visual Effects Generation | [
"Rui Zhao",
"Mike Zheng Shou"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Zhao_P-Flow_Prompting_Visual_Effects_Generation_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zhao_P-Flow_Prompting_Visual_Effects_Generation_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Zhao_P-Flow_Prompting_Visual_CVPR_2026_supplemental.pdf | 2603.22091 | cvf | @InProceedings{Zhao_2026_CVPR,
author = {Zhao, Rui and Shou, Mike Zheng},
title = {P-Flow: Prompting Visual Effects Generation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2026},
pages = {914... | Recent advancements in video generation models have significantly improved their ability to follow text prompts. However, the customization of dynamic visual effects, defined as temporally evolving and appearance-driven visual phenomena like object crushing or explosion, remains underexplored. Prior works on motion cus... |
Ehm_Teaching_DINOv3_About_Partial_3D_Geometry_A_Self-Supervised_Geometry-Aware_Approach_CVPR_2026_paper | Teaching DINOv3 About Partial 3D Geometry: A Self-Supervised Geometry-Aware Approach | [
"Viktoria Ehm",
"Dongliang Cao",
"Riccardo Marin",
"Daniel Scholz",
"Weikang Wang",
"Florian Bernard",
"Daniel Cremers"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Ehm_Teaching_DINOv3_About_Partial_3D_Geometry_A_Self-Supervised_Geometry-Aware_Approach_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Ehm_Teaching_DINOv3_About_Partial_3D_Geometry_A_Self-Supervised_Geometry-Aware_Approach_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Ehm_Teaching_DINOv3_About_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Ehm_2026_CVPR,
author = {Ehm, Viktoria and Cao, Dongliang and Marin, Riccardo and Scholz, Daniel and Wang, Weikang and Bernard, Florian and Cremers, Daniel},
title = {Teaching DINOv3 About Partial 3D Geometry: A Self-Supervised Geometry-Aware Approach},
booktitle = {Proceedings of the ... | Partial shape matching is a crucial yet underexplored problem in 3D vision, with significant relevance to real-world scenarios where shapes are often only partially observed. Existing feature descriptors face difficulties in this setting, as traditional representations either struggle with the boundaries of partial sha... |
Fa_One_Token_Two_Fates_A_Unified_Framework_via_Vision_Token_CVPR_2026_paper | One Token, Two Fates: A Unified Framework via Vision Token Manipulation Against MLLMs Hallucination | [
"Zhan Fa",
"Yue Duan",
"Jian Zhang",
"Lei Qi",
"Yinghuan Shi"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Fa_One_Token_Two_Fates_A_Unified_Framework_via_Vision_Token_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Fa_One_Token_Two_Fates_A_Unified_Framework_via_Vision_Token_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Fa_One_Token_Two_CVPR_2026_supplemental.pdf | 2603.10360 | cvf | @InProceedings{Fa_2026_CVPR,
author = {Fa, Zhan and Duan, Yue and Zhang, Jian and Qi, Lei and Shi, Yinghuan},
title = {One Token, Two Fates: A Unified Framework via Vision Token Manipulation Against MLLMs Hallucination},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patte... | Current training-free methods tackle MLLM hallucination with separate strategies: either enhancing visual signals or suppressing text inertia. However, these separate methods are insufficient due to critical trade-offs: simply enhancing vision often fails against strong language prior, while suppressing language can in... |
Liu_SpatialDiff_3D-Aware_Object_Movement_via_Implicit_Spatial_Modeling_CVPR_2026_paper | SpatialDiff: 3D-Aware Object Movement via Implicit Spatial Modeling | [
"Zheng Liu",
"Zijian He",
"Huiguo He",
"Weizhi Zhong",
"Yejun Tang",
"Huan Yang",
"Kun Gai",
"Guanbin Li"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Liu_SpatialDiff_3D-Aware_Object_Movement_via_Implicit_Spatial_Modeling_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Liu_SpatialDiff_3D-Aware_Object_Movement_via_Implicit_Spatial_Modeling_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Liu_SpatialDiff_3D-Aware_Object_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Liu_2026_CVPR,
author = {Liu, Zheng and He, Zijian and He, Huiguo and Zhong, Weizhi and Tang, Yejun and Yang, Huan and Gai, Kun and Li, Guanbin},
title = {SpatialDiff: 3D-Aware Object Movement via Implicit Spatial Modeling},
booktitle = {Proceedings of the IEEE/CVF Conference on Comput... | Recent advances in image editing allow impressive manipulation of objects, existing methods still struggle to handle spatial movement in complex scenes, such as objects span different depth layers or are partially occluded. Most image editing methods focus solely on prior information from 2D datasets, emphasizing plana... |
Huang_Voxify3D_Pixel_Art_Meets_Volumetric_Rendering_CVPR_2026_paper | Voxify3D: Pixel Art Meets Volumetric Rendering | [
"Yi-Chuan Huang",
"Jiewen Chan",
"Hao-Jen Chien",
"Yu-Lun Liu"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Huang_Voxify3D_Pixel_Art_Meets_Volumetric_Rendering_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Huang_Voxify3D_Pixel_Art_Meets_Volumetric_Rendering_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Huang_Voxify3D_Pixel_Art_CVPR_2026_supplemental.zip | 2512.07834 | cvf | @InProceedings{Huang_2026_CVPR,
author = {Huang, Yi-Chuan and Chan, Jiewen and Chien, Hao-Jen and Liu, Yu-Lun},
title = {Voxify3D: Pixel Art Meets Volumetric Rendering},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Voxel art is a distinctive stylization widely used in games and digital media, yet automated generation from 3D meshes remains challenging due to conflicting requirements of geometric abstraction, semantic preservation, and discrete color coherence. Existing methods either over-simplify geometry or fail to achieve the ... |
Zuo_Multi-modal_Frequency_Decomposition_Network_for_Semantic_Scene_Completion_CVPR_2026_paper | Multi-modal Frequency Decomposition Network for Semantic Scene Completion | [
"Die Zuo",
"Lubo Wang",
"Ruonan Liu",
"Qing Guo",
"Chong Wang",
"Dongdong Wu",
"Wei Feng",
"Kairui Yang",
"Di Lin"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Zuo_Multi-modal_Frequency_Decomposition_Network_for_Semantic_Scene_Completion_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zuo_Multi-modal_Frequency_Decomposition_Network_for_Semantic_Scene_Completion_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Zuo_Multi-modal_Frequency_Decomposition_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Zuo_2026_CVPR,
author = {Zuo, Die and Wang, Lubo and Liu, Ruonan and Guo, Qing and Wang, Chong and Wu, Dongdong and Feng, Wei and Yang, Kairui and Lin, Di},
title = {Multi-modal Frequency Decomposition Network for Semantic Scene Completion},
booktitle = {Proceedings of the IEEE/CVF Con... | Based on an RGB-D image pair, semantic scene completion (SSC) provides a description for 3D scene understanding by predicting 3D semantic occupancy map. Recent methods extract RGB-D multi-modal features and fuse them in spatial domain, which disregards the misalignment caused by the imperfect raw multi-modal data and t... |
Qiu_Beyond_Missing_Modalities_Hypergraph_Conditioned_Diffusion_for_Uncertainty-Aware_Multimodal_Emotion_CVPR_2026_paper | Beyond Missing Modalities: Hypergraph Conditioned Diffusion for Uncertainty-Aware Multimodal Emotion Recognition | [
"Xihang Qiu",
"Yuhao Fang",
"Qing Zhou",
"Bin Zhai",
"Jialong Hong",
"Wanpeng Zhang",
"Yao Lu",
"Ye Zhang",
"Chun Li"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Qiu_Beyond_Missing_Modalities_Hypergraph_Conditioned_Diffusion_for_Uncertainty-Aware_Multimodal_Emotion_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Qiu_Beyond_Missing_Modalities_Hypergraph_Conditioned_Diffusion_for_Uncertainty-Aware_Multimodal_Emotion_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Qiu_Beyond_Missing_Modalities_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Qiu_2026_CVPR,
author = {Qiu, Xihang and Fang, Yuhao and Zhou, Qing and Zhai, Bin and Hong, Jialong and Zhang, Wanpeng and Lu, Yao and Zhang, Ye and Li, Chun},
title = {Beyond Missing Modalities: Hypergraph Conditioned Diffusion for Uncertainty-Aware Multimodal Emotion Recognition},
bo... | Multimodal Emotion Recognition in Conversations (MERC) aims to understand emotions expressed in each utterance by effectively integrating audio, text, and visual modalities. However, in real-world scenarios, unavoidable missing modalities often degrade multimodal interpretation performance. To address this, we propose ... |
Wang_GRPO-Guard_Mitigating_Implicit_Over-Optimization_in_Flow_Matching_via_Regulated_Clipping_CVPR_2026_paper | GRPO-Guard: Mitigating Implicit Over-Optimization in Flow Matching via Regulated Clipping | [
"Jing Wang",
"Jiajun Liang",
"Jie Liu",
"Henglin Liu",
"Gongye Liu",
"Jun Zheng",
"Wanyuan Pang",
"Ao Ma",
"Zhenyu Xie",
"Xintao Wang",
"Meng Wang",
"Pengfei Wan",
"Xiaodan Liang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Wang_GRPO-Guard_Mitigating_Implicit_Over-Optimization_in_Flow_Matching_via_Regulated_Clipping_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Wang_GRPO-Guard_Mitigating_Implicit_Over-Optimization_in_Flow_Matching_via_Regulated_Clipping_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Wang_GRPO-Guard_Mitigating_Implicit_CVPR_2026_supplemental.pdf | 2510.22319 | cvf | @InProceedings{Wang_2026_CVPR,
author = {Wang, Jing and Liang, Jiajun and Liu, Jie and Liu, Henglin and Liu, Gongye and Zheng, Jun and Pang, Wanyuan and Ma, Ao and Xie, Zhenyu and Wang, Xintao and Wang, Meng and Wan, Pengfei and Liang, Xiaodan},
title = {GRPO-Guard: Mitigating Implicit Over-Optimization ... | Recently, GRPO-based reinforcement learning has shown remarkable progress in optimizing flow-matching models, effectively improving their alignment with task-specific rewards. Within these frameworks, the policy update relies on importance-ratio clipping to constrain overconfident positive and negative gradients. Howev... |
Yan_Target-Aware_Invertible_Encoder_with_Reconstruction_Guidance_for_Infrared_Small_Target_CVPR_2026_paper | Target-Aware Invertible Encoder with Reconstruction Guidance for Infrared Small Target Detection | [
"Shule Yan",
"Zetian Zhang",
"Xiao Ma",
"Zexuan Ji"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Yan_Target-Aware_Invertible_Encoder_with_Reconstruction_Guidance_for_Infrared_Small_Target_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Yan_Target-Aware_Invertible_Encoder_with_Reconstruction_Guidance_for_Infrared_Small_Target_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Yan_Target-Aware_Invertible_Encoder_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Yan_2026_CVPR,
author = {Yan, Shule and Zhang, Zetian and Ma, Xiao and Ji, Zexuan},
title = {Target-Aware Invertible Encoder with Reconstruction Guidance for Infrared Small Target Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | Modern detectors typically deepen backbones and rely on aggressive downsampling to harvest high-level semantics. But this severely degrades low-energy infrared tiny targets via rescale-induced information loss. This work introduces InvDet, a target-aware invertible encoder that unifies information preservation and targ... |
Liu_Cross-View_Distillation_and_Adaptive_Masking_for_Incomplete_Multi-View_Multi-Label_Classification_CVPR_2026_paper | Cross-View Distillation and Adaptive Masking for Incomplete Multi-View Multi-Label Classification | [
"Yadong Liu",
"Qiaoqi Li",
"Yueying Wang",
"Lunke Fei",
"Jie Wen"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Liu_Cross-View_Distillation_and_Adaptive_Masking_for_Incomplete_Multi-View_Multi-Label_Classification_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Liu_Cross-View_Distillation_and_Adaptive_Masking_for_Incomplete_Multi-View_Multi-Label_Classification_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Liu_Cross-View_Distillation_and_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Liu_2026_CVPR,
author = {Liu, Yadong and Li, Qiaoqi and Wang, Yueying and Fei, Lunke and Wen, Jie},
title = {Cross-View Distillation and Adaptive Masking for Incomplete Multi-View Multi-Label Classification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pa... | While existing incomplete multi-view multi-label learning methods have achieved promising performance, few studies have focused on the issue of multi-view imbalance. Existing methods using gradient modulation or alternating optimization strategies alleviate this problem but often oversimplify the interaction between vi... |
Kim_Efficient_Weighted_Sampling_via_Score-based_Generative_Models_CVPR_2026_paper | Efficient Weighted Sampling via Score-based Generative Models | [
"Heasung Kim",
"Taekyun Lee",
"Hyeji Kim",
"Gustavo De Veciana"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Kim_Efficient_Weighted_Sampling_via_Score-based_Generative_Models_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Kim_Efficient_Weighted_Sampling_via_Score-based_Generative_Models_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Kim_Efficient_Weighted_Sampling_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Kim_2026_CVPR,
author = {Kim, Heasung and Lee, Taekyun and Kim, Hyeji and De Veciana, Gustavo},
title = {Efficient Weighted Sampling via Score-based Generative Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Weighted sampling--sampling from a probability density function (PDF) proportional to the product of a base PDF and a weight function--is a fundamental technique with wide-ranging applications in variance reduction, biased sampling, data augmentation, and more. Leveraging the increasing availability of pretrained score... |
Li_SD-FSMIS_Adapting_Stable_Diffusion_for_Few-Shot_Medical_Image_Segmentation_CVPR_2026_paper | SD-FSMIS: Adapting Stable Diffusion for Few-Shot Medical Image Segmentation | [
"Meihua Li",
"Yang Zhang",
"Weizhao He",
"Hu Qu",
"Yisong Li"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Li_SD-FSMIS_Adapting_Stable_Diffusion_for_Few-Shot_Medical_Image_Segmentation_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Li_SD-FSMIS_Adapting_Stable_Diffusion_for_Few-Shot_Medical_Image_Segmentation_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Li_SD-FSMIS_Adapting_Stable_CVPR_2026_supplemental.pdf | 2604.03134 | cvf | @InProceedings{Li_2026_CVPR,
author = {Li, Meihua and Zhang, Yang and He, Weizhao and Qu, Hu and Li, Yisong},
title = {SD-FSMIS: Adapting Stable Diffusion for Few-Shot Medical Image Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Few-Shot Medical Image Segmentation (FSMIS) aims to segment novel object classes in medical images using only minimal annotated examples, addressing the critical challenges of data scarcity and domain shifts prevalent in medical imaging. While Diffusion Models (DM) excel in visual tasks, their potential for FSMIS remai... |
Shao_FastLightGen_Fast_and_Light_Video_Generation_with_Fewer_Steps_and_CVPR_2026_paper | FastLightGen: Fast and Light Video Generation with Fewer Steps and Parameters | [
"Shitong Shao",
"Yufei Gu",
"Zeke Xie"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Shao_FastLightGen_Fast_and_Light_Video_Generation_with_Fewer_Steps_and_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Shao_FastLightGen_Fast_and_Light_Video_Generation_with_Fewer_Steps_and_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Shao_FastLightGen_Fast_and_CVPR_2026_supplemental.pdf | 2603.01685 | cvf | @InProceedings{Shao_2026_CVPR,
author = {Shao, Shitong and Gu, Yufei and Xie, Zeke},
title = {FastLightGen: Fast and Light Video Generation with Fewer Steps and Parameters},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},... | The recent advent of powerful video generation models, such as Hunyuan, WanX, Veo3, and Kling, has inaugurated a new era in the field. However, the practical deployment of these models is severely impeded by their substantial computational overhead, which stems from enormous parameter counts and the iterative, multi-st... |
Anthony_The_Invisible_Gorilla_Effect_in_Out-of-distribution_Detection_CVPR_2026_paper | The Invisible Gorilla Effect in Out-of-distribution Detection | [
"Harry Anthony",
"Ziyun Liang",
"Hermione Warr",
"Konstantinos Kamnitsas"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Anthony_The_Invisible_Gorilla_Effect_in_Out-of-distribution_Detection_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Anthony_The_Invisible_Gorilla_Effect_in_Out-of-distribution_Detection_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Anthony_The_Invisible_Gorilla_CVPR_2026_supplemental.pdf | 2602.20068 | cvf | @InProceedings{Anthony_2026_CVPR,
author = {Anthony, Harry and Liang, Ziyun and Warr, Hermione and Kamnitsas, Konstantinos},
title = {The Invisible Gorilla Effect in Out-of-distribution Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},... | Deep Neural Networks achieve high performance in vision tasks by learning features from regions of interest (ROI) within images, but their performance degrades when deployed on out-of-distribution (OOD) data that differs from training data. This challenge has led to OOD detection methods that aim to identify and reject... |
Kong_Learning_by_Analogy_A_Causal_Framework_for_Compositional_Generalization_CVPR_2026_paper | Learning by Analogy: A Causal Framework for Compositional Generalization | [
"Lingjing Kong",
"Shaoan Xie",
"Yang Jiao",
"Yetian Chen",
"Yanhui Guo",
"Simone Shao",
"Yan Gao",
"Guangyi Chen",
"Kun Zhang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Kong_Learning_by_Analogy_A_Causal_Framework_for_Compositional_Generalization_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Kong_Learning_by_Analogy_A_Causal_Framework_for_Compositional_Generalization_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Kong_Learning_by_Analogy_CVPR_2026_supplemental.pdf | 2512.10669 | cvf | @InProceedings{Kong_2026_CVPR,
author = {Kong, Lingjing and Xie, Shaoan and Jiao, Yang and Chen, Yetian and Guo, Yanhui and Shao, Simone and Gao, Yan and Chen, Guangyi and Zhang, Kun},
title = {Learning by Analogy: A Causal Framework for Compositional Generalization},
booktitle = {Proceedings of the ... | Compositional generalization -- the ability to understand and generate novel combinations of learned concepts -- enables models to extend their capabilities beyond limited experiences. While effective, the data structures and principles that enable this crucial capability remain poorly understood. We propose that compo... |
Zhang_Prefill-Time_Intervention_for_Mitigating_Hallucination_in_Large_Vision-Language_Models_CVPR_2026_paper | Prefill-Time Intervention for Mitigating Hallucination in Large Vision-Language Models | [
"Chengsheng Zhang",
"Chenghao Sun",
"Xinyan Jiang",
"Wei Li",
"Xinmei Tian"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Zhang_Prefill-Time_Intervention_for_Mitigating_Hallucination_in_Large_Vision-Language_Models_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zhang_Prefill-Time_Intervention_for_Mitigating_Hallucination_in_Large_Vision-Language_Models_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Zhang_Prefill-Time_Intervention_for_CVPR_2026_supplemental.pdf | 2604.25642 | cvf | @InProceedings{Zhang_2026_CVPR,
author = {Zhang, Chengsheng and Sun, Chenghao and Jiang, Xinyan and Li, Wei and Tian, Xinmei},
title = {Prefill-Time Intervention for Mitigating Hallucination in Large Vision-Language Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and P... | Large Vision-Language Models (LVLMs) have achieved remarkable progress in visual-textual understanding, yet their reliability is critically undermined by hallucinations, i.e., the generation of factually incorrect or inconsistent responses.While recent studies using steering vectors demonstrated promise in reducing hal... |
He_UniPart_Part-Level_3D_Generation_with_Unified_3D_Geom-Seg_Latents_CVPR_2026_paper | UniPart: Part-Level 3D Generation with Unified 3D Geom-Seg Latents | [
"Xufan He",
"Yushuang Wu",
"Xiaoyang Guo",
"Chongjie Ye",
"Jiaqing Zhou",
"Tianlei Hu",
"Xiaoguang Han",
"Dong Du"
] | https://openaccess.thecvf.com/content/CVPR2026/html/He_UniPart_Part-Level_3D_Generation_with_Unified_3D_Geom-Seg_Latents_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/He_UniPart_Part-Level_3D_Generation_with_Unified_3D_Geom-Seg_Latents_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/He_UniPart_Part-Level_3D_CVPR_2026_supplemental.pdf | 2512.09435 | cvf | @InProceedings{He_2026_CVPR,
author = {He, Xufan and Wu, Yushuang and Guo, Xiaoyang and Ye, Chongjie and Zhou, Jiaqing and Hu, Tianlei and Han, Xiaoguang and Du, Dong},
title = {UniPart: Part-Level 3D Generation with Unified 3D Geom-Seg Latents},
booktitle = {Proceedings of the IEEE/CVF Conference on... | Part-level 3D generation is essential for applications requiring decomposable and structured 3D synthesis. However, existing methods either rely on implicit part segmentation with limited granularity control or depend on strong external segmenters trained on large annotated datasets. In this work, we observe that part ... |
Zhan_Rotation_Invariant_and_Symmetry_Aware_Pixel_Difference_Network_for_Remote_CVPR_2026_paper | Rotation Invariant and Symmetry Aware Pixel Difference Network for Remote Sensing Object Detection | [
"Jialei Zhan",
"Li Liu",
"Jiehua Zhang",
"Yuhang Xie",
"Yongxiang Liu",
"Jiangming Chen",
"Ming-Ming Cheng"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Zhan_Rotation_Invariant_and_Symmetry_Aware_Pixel_Difference_Network_for_Remote_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zhan_Rotation_Invariant_and_Symmetry_Aware_Pixel_Difference_Network_for_Remote_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Zhan_Rotation_Invariant_and_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Zhan_2026_CVPR,
author = {Zhan, Jialei and Liu, Li and Zhang, Jiehua and Xie, Yuhang and Liu, Yongxiang and Chen, Jiangming and Cheng, Ming-Ming},
title = {Rotation Invariant and Symmetry Aware Pixel Difference Network for Remote Sensing Object Detection},
booktitle = {Proceedings of t... | Recent advancements in remote sensing object detection have predominantly focused on oriented bounding box design and small object feature enhancement, while often overlooking the intrinsic geometric properties of remote sensing images, such as rotation invariance and structural symmetry. Many aerial objects appear in ... |
Zhou_Gamba_Mamba-based_graph_convolutional_network_with_dynamic_graph_topology_learning_CVPR_2026_paper | Gamba: Mamba-based graph convolutional network with dynamic graph topology learning for action recognition | [
"Rouyi Zhou",
"Yangzhi Wu",
"Jiajun Wen",
"Can Gao",
"Feng Liu",
"Zhihui Lai",
"Linlin Shen"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Zhou_Gamba_Mamba-based_graph_convolutional_network_with_dynamic_graph_topology_learning_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zhou_Gamba_Mamba-based_graph_convolutional_network_with_dynamic_graph_topology_learning_CVPR_2026_paper.pdf | null | null | null | @InProceedings{Zhou_2026_CVPR,
author = {Zhou, Rouyi and Wu, Yangzhi and Wen, Jiajun and Gao, Can and Liu, Feng and Lai, Zhihui and Shen, Linlin},
title = {Gamba: Mamba-based graph convolutional network with dynamic graph topology learning for action recognition},
booktitle = {Proceedings of the IEEE... | Existing graph models predominantly utilize self-attention mechanisms to model feature correlations between the joints of each sample, which not only neglects dynamic relation dependencies in temporal dimension but also leads to redundant computation and difficulty in establishing a unified framework for joint relation... |
Ning_Language-Guided_One-Step_Diffusion_Model_for_Nighttime_Flare_Removal_CVPR_2026_paper | Language-Guided One-Step Diffusion Model for Nighttime Flare Removal | [
"Aoxiang Ning",
"Kailong Yu",
"Minglong Xue",
"Liyuan Pan",
"Jinhong He",
"Wenchao Yan",
"Mingliang Zhou",
"Yirui Wu"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Ning_Language-Guided_One-Step_Diffusion_Model_for_Nighttime_Flare_Removal_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Ning_Language-Guided_One-Step_Diffusion_Model_for_Nighttime_Flare_Removal_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Ning_Language-Guided_One-Step_Diffusion_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Ning_2026_CVPR,
author = {Ning, Aoxiang and Yu, Kailong and Xue, Minglong and Pan, Liyuan and He, Jinhong and Yan, Wenchao and Zhou, Mingliang and Wu, Yirui},
title = {Language-Guided One-Step Diffusion Model for Nighttime Flare Removal},
booktitle = {Proceedings of the IEEE/CVF Confer... | Nighttime photography is susceptible to flare caused by strong light sources, which degrades visual quality and disrupts structural information required by downstream vision tasks. Existing nighttime flare removal methods generally lack semantic priors for flare-occluded regions and thus tend to introduce artifacts and... |
Sun_Instance-level_Visual_Active_Tracking_with_Occlusion-Aware_Planning_CVPR_2026_paper | Instance-level Visual Active Tracking with Occlusion-Aware Planning | [
"Haowei Sun",
"Kai Zhou",
"Hao Gao",
"Shiteng Zhang",
"Jinwu Hu",
"Xutao Wen",
"Qixiang Ye",
"Mingkui Tan"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Sun_Instance-level_Visual_Active_Tracking_with_Occlusion-Aware_Planning_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Sun_Instance-level_Visual_Active_Tracking_with_Occlusion-Aware_Planning_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Sun_Instance-level_Visual_Active_CVPR_2026_supplemental.pdf | 2604.21453 | cvf | @InProceedings{Sun_2026_CVPR,
author = {Sun, Haowei and Zhou, Kai and Gao, Hao and Zhang, Shiteng and Hu, Jinwu and Wen, Xutao and Ye, Qixiang and Tan, Mingkui},
title = {Instance-level Visual Active Tracking with Occlusion-Aware Planning},
booktitle = {Proceedings of the IEEE/CVF Conference on Compu... | Visual Active Tracking (VAT) aims to control cameras to follow a target in 3D space, which is critical for applications like drone navigation and security surveillance. However, it faces two key bottlenecks in real-world deployment: confusion from visually similar distractors caused by insufficient instance-level discr... |
Zhang_HandX_Scaling_Bimanual_Motion_and_Interaction_Generation_CVPR_2026_paper | HandX: Scaling Bimanual Motion and Interaction Generation | [
"Zimu Zhang",
"Yucheng Zhang",
"Xiyan Xu",
"Ziyin Wang",
"Sirui Xu",
"Kai Zhou",
"Bing Zhou",
"Chuan Guo",
"Jian Wang",
"Yu-Xiong Wang",
"Liang-Yan Gui"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Zhang_HandX_Scaling_Bimanual_Motion_and_Interaction_Generation_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zhang_HandX_Scaling_Bimanual_Motion_and_Interaction_Generation_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Zhang_HandX_Scaling_Bimanual_CVPR_2026_supplemental.pdf | 2603.28766 | cvf | @InProceedings{Zhang_2026_CVPR,
author = {Zhang, Zimu and Zhang, Yucheng and Xu, Xiyan and Wang, Ziyin and Xu, Sirui and Zhou, Kai and Zhou, Bing and Guo, Chuan and Wang, Jian and Wang, Yu-Xiong and Gui, Liang-Yan},
title = {HandX: Scaling Bimanual Motion and Interaction Generation},
booktitle = {Pro... | Synthesizing human motion has advanced rapidly, yet realistic hand motion and bimanual interaction remain underexplored. Whole-body models often miss the fine-grained cues that drive dexterous behavior, finger articulation, contact timing, and inter-hand coordination, and existing resources lack high-fidelity bimanual ... |
Xu_GeoDiff4D_Geometry-Aware_Diffusion_for_4D_Head_Avatar_Reconstruction_CVPR_2026_paper | GeoDiff4D: Geometry-Aware Diffusion for 4D Head Avatar Reconstruction | [
"Chao Xu",
"Xiaochen Zhao",
"Xiang Deng",
"Jingxiang Sun",
"Donglin Di",
"Zhuo Su",
"Yebin Liu"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Xu_GeoDiff4D_Geometry-Aware_Diffusion_for_4D_Head_Avatar_Reconstruction_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Xu_GeoDiff4D_Geometry-Aware_Diffusion_for_4D_Head_Avatar_Reconstruction_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Xu_GeoDiff4D_Geometry-Aware_Diffusion_CVPR_2026_supplemental.pdf | 2602.24161 | cvf | @InProceedings{Xu_2026_CVPR,
author = {Xu, Chao and Zhao, Xiaochen and Deng, Xiang and Sun, Jingxiang and Di, Donglin and Su, Zhuo and Liu, Yebin},
title = {GeoDiff4D: Geometry-Aware Diffusion for 4D Head Avatar Reconstruction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision a... | Reconstructing photorealistic and animatable 4D head avatars from a single portrait image remains a fundamental challenge in computer vision. While diffusion models have enabled remarkable progress in image and video generation for avatar reconstruction, existing methods primarily rely on 2D priors and struggle to achi... |
Huang_LLaVAShield_Safeguarding_Multimodal_Multi-Turn_Dialogues_in_Vision-Language_Models_CVPR_2026_paper | LLaVAShield: Safeguarding Multimodal Multi-Turn Dialogues in Vision-Language Models | [
"Guolei Huang",
"Qinzhi Peng",
"Gan Xu",
"Yao Huang",
"Yuxuan Lu",
"Yongjun Shen"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Huang_LLaVAShield_Safeguarding_Multimodal_Multi-Turn_Dialogues_in_Vision-Language_Models_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Huang_LLaVAShield_Safeguarding_Multimodal_Multi-Turn_Dialogues_in_Vision-Language_Models_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Huang_LLaVAShield_Safeguarding_Multimodal_CVPR_2026_supplemental.pdf | 2509.25896 | cvf | @InProceedings{Huang_2026_CVPR,
author = {Huang, Guolei and Peng, Qinzhi and Xu, Gan and Huang, Yao and Lu, Yuxuan and Shen, Yongjun},
title = {LLaVAShield: Safeguarding Multimodal Multi-Turn Dialogues in Vision-Language Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision ... | As Vision-Language Models (VLMs) move into interactive, multi-turn use, safety concerns intensify for multimodal multi-turn dialogue, which is characterized by concealment of malicious intent, contextual risk accumulation, and cross-modal joint risk. These characteristics limit the effectiveness of content moderation a... |
Zhou_R2G_A_Multi-View_Circuit_Graph_Benchmark_Suite_from_RTL_to_CVPR_2026_paper | R2G: A Multi-View Circuit Graph Benchmark Suite from RTL to GDSII | [
"Zewei Zhou",
"Jiajun Zou",
"Jiajia Zhang",
"Ao Yang",
"Ruichao He",
"Haozheng Zhou",
"Ao Liu",
"Jiawei Liu",
"Leilei Jin",
"Shan Shen",
"Daying Sun"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Zhou_R2G_A_Multi-View_Circuit_Graph_Benchmark_Suite_from_RTL_to_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zhou_R2G_A_Multi-View_Circuit_Graph_Benchmark_Suite_from_RTL_to_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Zhou_R2G_A_Multi-View_CVPR_2026_supplemental.pdf | 2604.08810 | cvf | @InProceedings{Zhou_2026_CVPR,
author = {Zhou, Zewei and Zou, Jiajun and Zhang, Jiajia and Yang, Ao and He, Ruichao and Zhou, Haozheng and Liu, Ao and Liu, Jiawei and Jin, Leilei and Shen, Shan and Sun, Daying},
title = {R2G: A Multi-View Circuit Graph Benchmark Suite from RTL to GDSII},
booktitle = ... | Graph neural networks (GNNs) are increasingly applied to physical design tasks such as congestion prediction and wirelength estimation, yet progress is hindered by inconsistent circuit representations and the absence of controlled evaluation protocols. We present R2G (RTL-to-GDSII), a multi-view circuit-graph benchmark... |
Sun_Time-Specialized_Event-Image_Alignment_for_Blur-to-Video_Decomposition_CVPR_2026_paper | Time-Specialized Event-Image Alignment for Blur-to-Video Decomposition | [
"Zhijing Sun",
"Senyan Xu",
"Ruixuan Jiang",
"Kean Liu",
"Runze Tian",
"Xueyang Fu",
"Zheng-Jun Zha"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Sun_Time-Specialized_Event-Image_Alignment_for_Blur-to-Video_Decomposition_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Sun_Time-Specialized_Event-Image_Alignment_for_Blur-to-Video_Decomposition_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Sun_Time-Specialized_Event-Image_Alignment_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Sun_2026_CVPR,
author = {Sun, Zhijing and Xu, Senyan and Jiang, Ruixuan and Liu, Kean and Tian, Runze and Fu, Xueyang and Zha, Zheng-Jun},
title = {Time-Specialized Event-Image Alignment for Blur-to-Video Decomposition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer V... | Motion blur is a common degradation in dynamic imaging. Recent studies have moved beyond restoring a single sharp image from a blurred input and instead target blur decomposition: recovering a temporally continuous sharp video sequence from one motion-blurred image. Event cameras, with their microsecond temporal resolu... |
Kuang_JUMP-Hand_Learning_Joint-wise_Uncertainty_to_Gate_Mixture_of_View_Experts_CVPR_2026_paper | JUMP-Hand: Learning Joint-wise Uncertainty to Gate Mixture of View Experts for Multi-View 3D Hand Reconstruction | [
"Haohong Kuang",
"Yang Xiao",
"Changlong Jiang",
"Jinghong Zheng",
"Hang Xu",
"Ran Wang",
"Zhiguo Cao",
"Joey Tianyi Zhou"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Kuang_JUMP-Hand_Learning_Joint-wise_Uncertainty_to_Gate_Mixture_of_View_Experts_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Kuang_JUMP-Hand_Learning_Joint-wise_Uncertainty_to_Gate_Mixture_of_View_Experts_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Kuang_JUMP-Hand_Learning_Joint-wise_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Kuang_2026_CVPR,
author = {Kuang, Haohong and Xiao, Yang and Jiang, Changlong and Zheng, Jinghong and Xu, Hang and Wang, Ran and Cao, Zhiguo and Zhou, Joey Tianyi},
title = {JUMP-Hand: Learning Joint-wise Uncertainty to Gate Mixture of View Experts for Multi-View 3D Hand Reconstruction},
... | We propose JUMP-Hand, a novel multi-view 3D hand reconstruction method that explicitly models probabilistic joint-wise uncertainty as a gating mechanism for multi-view fusion. Existing approaches usually rely on naive pooling or implicit attention, overlooking that each hand joint exhibits varying visibility and reliab... |
Gu_From_Manuals_to_Actions_A_Unified_VLA_Model_for_Chain-of-Thought_CVPR_2026_paper | From Manuals to Actions: A Unified VLA Model for Chain-of-Thought Manual Generation and Robotic Manipulation | [
"Chenyang Gu",
"Jiaming Liu",
"Hao Chen",
"Runzhong Huang",
"Qingpo Wuwu",
"Xiaoqi Li",
"Zhuoyang Liu",
"Ying Li",
"Renrui Zhang",
"Peng Jia",
"Pheng-Ann Heng",
"Shanghang Zhang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Gu_From_Manuals_to_Actions_A_Unified_VLA_Model_for_Chain-of-Thought_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Gu_From_Manuals_to_Actions_A_Unified_VLA_Model_for_Chain-of-Thought_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Gu_From_Manuals_to_CVPR_2026_supplemental.zip | 2512.02013 | title_judge | @InProceedings{Gu_2026_CVPR,
author = {Gu, Chenyang and Liu, Jiaming and Chen, Hao and Huang, Runzhong and Wuwu, Qingpo and Li, Xiaoqi and Liu, Zhuoyang and Li, Ying and Zhang, Renrui and Jia, Peng and Heng, Pheng-Ann and Zhang, Shanghang},
title = {From Manuals to Actions: A Unified VLA Model for Chain-... | Vision-Language-Action (VLA) models have recently emerged, demonstrating strong generalization in robotic scene understanding and manipulation. However, when confronted with long-horizon tasks that require defined goal states, such as LEGO assembly or object rearrangement, existing VLA models still face challenges in c... |
Cui_Anatomical_Domain_Shifts_Test-time_Heterogeneous_Adaptation_for_3D_Human_Pose_CVPR_2026_paper | Anatomical Domain Shifts: Test-time Heterogeneous Adaptation for 3D Human Pose Prediction | [
"Qiongjie Cui",
"Pan Zhou",
"Jingjing Chen",
"Na Zhao"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Cui_Anatomical_Domain_Shifts_Test-time_Heterogeneous_Adaptation_for_3D_Human_Pose_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Cui_Anatomical_Domain_Shifts_Test-time_Heterogeneous_Adaptation_for_3D_Human_Pose_CVPR_2026_paper.pdf | null | null | null | @InProceedings{Cui_2026_CVPR,
author = {Cui, Qiongjie and Zhou, Pan and Chen, Jingjing and Zhao, Na},
title = {Anatomical Domain Shifts: Test-time Heterogeneous Adaptation for 3D Human Pose Prediction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CV... | The research frontier in human pose prediction (HPP) is advancing toward continual test-time adaptation (TTA), where models must self-adapt to dynamic test distributions. To date, the homeostatic continual TTA remains the sole viable solution, which isolates the model parameters and update domain-sensitive ones. Despit... |
Tsai_Pointing_at_Parts_Training-Free_Few-Shot_Grounding_in_Multimodal_LLMs_CVPR_2026_paper | Pointing at Parts: Training-Free Few-Shot Grounding in Multimodal LLMs | [
"Shiang-Feng Tsai",
"Yuan-Hong Liao",
"Jin-Cheng Jhang",
"Nan Qiao",
"Min Sun"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Tsai_Pointing_at_Parts_Training-Free_Few-Shot_Grounding_in_Multimodal_LLMs_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Tsai_Pointing_at_Parts_Training-Free_Few-Shot_Grounding_in_Multimodal_LLMs_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Tsai_Pointing_at_Parts_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Tsai_2026_CVPR,
author = {Tsai, Shiang-Feng and Liao, Yuan-Hong and Jhang, Jin-Cheng and Qiao, Nan and Sun, Min},
title = {Pointing at Parts: Training-Free Few-Shot Grounding in Multimodal LLMs},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni... | Part-level pointing is important for fine-grained interaction and reasoning, yet existing Multimodal Large Language Models (MLLMs) remain limited to instance-level pointing. Part-level pointing presents unique challenges: annotation is costly, parts are long-tail distributed, and many are difficult to specify precisely... |
Lendering_SubspaceAD_Training-Free_Few-Shot_Anomaly_Detection_via_Subspace_Modeling_CVPR_2026_paper | SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling | [
"Camile Lendering",
"Erkut Akdag",
"Egor Bondarau"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Lendering_SubspaceAD_Training-Free_Few-Shot_Anomaly_Detection_via_Subspace_Modeling_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Lendering_SubspaceAD_Training-Free_Few-Shot_Anomaly_Detection_via_Subspace_Modeling_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Lendering_SubspaceAD_Training-Free_Few-Shot_CVPR_2026_supplemental.pdf | 2602.23013 | title_snapshot | @InProceedings{Lendering_2026_CVPR,
author = {Lendering, Camile and Akdag, Erkut and Bondarau, Egor},
title = {SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month... | Detecting visual anomalies in industrial inspection often requires training with only a few normal images per category. Recent few-shot methods achieve strong results employing foundation-model features, but typically rely on memory banks, auxiliary datasets, or multi-modal tuning of vision-language models. We therefor... |
Zhang_Evolving_Contextual_Safety_in_Multi-Modal_Large_Language_Models_via_Inference-Time_CVPR_2026_paper | Evolving Contextual Safety in Multi-Modal Large Language Models via Inference-Time Self-Reflective Memory | [
"Ce Zhang",
"Jinxi He",
"Junyi He",
"Katia Sycara",
"Yaqi Xie"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Zhang_Evolving_Contextual_Safety_in_Multi-Modal_Large_Language_Models_via_Inference-Time_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zhang_Evolving_Contextual_Safety_in_Multi-Modal_Large_Language_Models_via_Inference-Time_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Zhang_Evolving_Contextual_Safety_CVPR_2026_supplemental.pdf | 2603.15800 | cvf | @InProceedings{Zhang_2026_CVPR,
author = {Zhang, Ce and He, Jinxi and He, Junyi and Sycara, Katia and Xie, Yaqi},
title = {Evolving Contextual Safety in Multi-Modal Large Language Models via Inference-Time Self-Reflective Memory},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision... | Multi-modal Large Language Models (MLLMs) have achieved remarkable performance across a wide range of visual reasoning tasks, yet their vulnerability to safety risks remains a pressing concern. While prior research primarily focuses on jailbreak defenses that detect and refuse explicitly unsafe inputs, such approaches ... |
Shen_FlashMesh_Faster_and_Better_Autoregressive_Mesh_Synthesis_via_Structured_Speculation_CVPR_2026_paper | FlashMesh: Faster and Better Autoregressive Mesh Synthesis via Structured Speculation | [
"Tingrui Shen",
"Yiheng Zhang",
"Chen Tang",
"Chuan Ping",
"Zixing Zhao",
"Le Wan",
"Yuwang Wang",
"Ronggang Wang",
"Shengfeng He"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Shen_FlashMesh_Faster_and_Better_Autoregressive_Mesh_Synthesis_via_Structured_Speculation_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Shen_FlashMesh_Faster_and_Better_Autoregressive_Mesh_Synthesis_via_Structured_Speculation_CVPR_2026_paper.pdf | null | 2511.15618 | cvf | @InProceedings{Shen_2026_CVPR,
author = {Shen, Tingrui and Zhang, Yiheng and Tang, Chen and Ping, Chuan and Zhao, Zixing and Wan, Le and Wang, Yuwang and Wang, Ronggang and He, Shengfeng},
title = {FlashMesh: Faster and Better Autoregressive Mesh Synthesis via Structured Speculation},
booktitle = {Pr... | Autoregressive models can generate high-quality 3D meshes by sequentially producing vertices and faces, but their token-by-token decoding results in slow inference, limiting practical use in interactive and large-scale applications.We present FlashMesh, a fast and high-fidelity mesh generation framework that rethinks a... |
Agrawal_SeeThrough3D_Occlusion_Aware_3D_Control_in_Text-to-Image_Generation_CVPR_2026_paper | SeeThrough3D: Occlusion Aware 3D Control in Text-to-Image Generation | [
"Vaibhav Agrawal",
"Rishubh Parihar",
"Pradhaan S Bhat",
"Ravi Kiran Sarvadevabhatla",
"Venkatesh Babu Radhakrishnan"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Agrawal_SeeThrough3D_Occlusion_Aware_3D_Control_in_Text-to-Image_Generation_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Agrawal_SeeThrough3D_Occlusion_Aware_3D_Control_in_Text-to-Image_Generation_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Agrawal_SeeThrough3D_Occlusion_Aware_CVPR_2026_supplemental.zip | 2602.23359 | cvf | @InProceedings{Agrawal_2026_CVPR,
author = {Agrawal, Vaibhav and Parihar, Rishubh and Bhat, Pradhaan S and Sarvadevabhatla, Ravi Kiran and Radhakrishnan, Venkatesh Babu},
title = {SeeThrough3D: Occlusion Aware 3D Control in Text-to-Image Generation},
booktitle = {Proceedings of the IEEE/CVF Conferenc... | We identify occlusion reasoning as a fundamental yet overlooked aspect for 3D layout-conditioned generation. It is essential for synthesizing partially occluded objects with depth-consistent geometry and scale. While existing methods can generate realistic scenes that follow input layouts, they often fail to model prec... |
Zhang_Exemplar-Free_Class_Incremental_Learning_via_Preserving_Class-Discriminative_Structure_CVPR_2026_paper | Exemplar-Free Class Incremental Learning via Preserving Class-Discriminative Structure | [
"Xin Zhang",
"Liang Bai",
"Guanchao Wang",
"Xian Yang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Zhang_Exemplar-Free_Class_Incremental_Learning_via_Preserving_Class-Discriminative_Structure_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zhang_Exemplar-Free_Class_Incremental_Learning_via_Preserving_Class-Discriminative_Structure_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Zhang_Exemplar-Free_Class_Incremental_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Zhang_2026_CVPR,
author = {Zhang, Xin and Bai, Liang and Wang, Guanchao and Yang, Xian},
title = {Exemplar-Free Class Incremental Learning via Preserving Class-Discriminative Structure},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVP... | Exemplar-Free Class Incremental Learning (EFCIL) aims to enable models to learn new classes sequentially without retaining samples from previous tasks. While recent approaches leverage pre-trained models with parameter-efficient tuning to mitigate forgetting, they often overlook a crucial cause of forgetting: the colla... |
Park_RARE_Learn_to_RAnk_and_REtrieve_for_Monocular_3D_Object_CVPR_2026_paper | RARE: Learn to RAnk and REtrieve for Monocular 3D Object Detection | [
"Hyeonjeong Park",
"Peixi Xiong",
"Xiaoqian Ruan",
"Dian Jia",
"Pei Yu",
"Wei Tang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Park_RARE_Learn_to_RAnk_and_REtrieve_for_Monocular_3D_Object_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Park_RARE_Learn_to_RAnk_and_REtrieve_for_Monocular_3D_Object_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Park_RARE_Learn_to_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Park_2026_CVPR,
author = {Park, Hyeonjeong and Xiong, Peixi and Ruan, Xiaoqian and Jia, Dian and Yu, Pei and Tang, Wei},
title = {RARE: Learn to RAnk and REtrieve for Monocular 3D Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco... | Monocular 3D object detection from a single RGB image remains challenging due to two fundamental challenges: the ill-posed nature of 3D localization, where multiple plausible configurations can correspond to the same 2D observation, and unreliable confidence estimation that fails to reflect true localization accuracy. ... |
Choi_Do_You_See_What_I_Am_Pointing_At_Gesture-Based_Egocentric_CVPR_2026_paper | Do You See What I Am Pointing At? Gesture-Based Egocentric Video Question Answering | [
"Yura Choi",
"Roy Miles",
"Rolandos Alexandros Potamias",
"Ismail Elezi",
"Jiankang Deng",
"Stefanos Zafeiriou"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Choi_Do_You_See_What_I_Am_Pointing_At_Gesture-Based_Egocentric_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Choi_Do_You_See_What_I_Am_Pointing_At_Gesture-Based_Egocentric_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Choi_Do_You_See_CVPR_2026_supplemental.pdf | 2603.12533 | cvf | @InProceedings{Choi_2026_CVPR,
author = {Choi, Yura and Miles, Roy and Potamias, Rolandos Alexandros and Elezi, Ismail and Deng, Jiankang and Zafeiriou, Stefanos},
title = {Do You See What I Am Pointing At? Gesture-Based Egocentric Video Question Answering},
booktitle = {Proceedings of the IEEE/CVF C... | Understanding and answering questions based on a user's pointing gesture is essential for next-generation egocentric AI assistants. However, current Multimodal Large Language Models (MLLMs) struggle with such tasks due to the lack of gesture-rich data and their limited ability to infer fine-grained pointing intent from... |
Noda_3D_Gaussian_Splatting_with_Self-Constrained_Priors_for_High_Fidelity_Surface_CVPR_2026_paper | 3D Gaussian Splatting with Self-Constrained Priors for High Fidelity Surface Reconstruction | [
"Takeshi Noda",
"Yu-Shen Liu",
"Zhizhong Han"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Noda_3D_Gaussian_Splatting_with_Self-Constrained_Priors_for_High_Fidelity_Surface_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Noda_3D_Gaussian_Splatting_with_Self-Constrained_Priors_for_High_Fidelity_Surface_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Noda_3D_Gaussian_Splatting_CVPR_2026_supplemental.zip | 2603.19682 | cvf | @InProceedings{Noda_2026_CVPR,
author = {Noda, Takeshi and Liu, Yu-Shen and Han, Zhizhong},
title = {3D Gaussian Splatting with Self-Constrained Priors for High Fidelity Surface Reconstruction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Rendering 3D surfaces has been revolutionized within the modeling of radiance fields through either 3DGS or NeRF. Although 3DGS has shown advantages over NeRF in terms of rendering quality or speed, there is still room for improvement in recovering high fidelity surfaces through 3DGS. To resolve this issue, we propose ... |
He_VideoSSR_Video_Self-Supervised_Reinforcement_Learning_CVPR_2026_paper | VideoSSR: Video Self-Supervised Reinforcement Learning | [
"Zefeng He",
"Xiaoye Qu",
"Yafu Li",
"Siyuan Huang",
"Daizong Liu",
"Yu Cheng"
] | https://openaccess.thecvf.com/content/CVPR2026/html/He_VideoSSR_Video_Self-Supervised_Reinforcement_Learning_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/He_VideoSSR_Video_Self-Supervised_Reinforcement_Learning_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/He_VideoSSR_Video_Self-Supervised_CVPR_2026_supplemental.pdf | 2511.06281 | cvf | @InProceedings{He_2026_CVPR,
author = {He, Zefeng and Qu, Xiaoye and Li, Yafu and Huang, Siyuan and Liu, Daizong and Cheng, Yu},
title = {VideoSSR: Video Self-Supervised Reinforcement Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Reinforcement Learning with Verifiable Reward (RLVR) has substantially advanced the video understanding capabilities of Multimodal Large Language Models (MLLMs). However, the rapid progress of MLLMs is outpacing the complexity of existing video datasets, while the manual annotation of new, high-quality data remains pro... |
Pan_Reasoning_Diffusion_for_Unpaired_Test_Time_Out-of-distribution_Text-Image_to_Video_CVPR_2026_paper | Reasoning Diffusion for Unpaired Test Time Out-of-distribution Text-Image to Video Generation | [
"Zirui Pan",
"Xin Wang",
"Yipeng Zhang",
"Hong Chen",
"Kecheng Zheng",
"Wenwu Zhu"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Pan_Reasoning_Diffusion_for_Unpaired_Test_Time_Out-of-distribution_Text-Image_to_Video_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Pan_Reasoning_Diffusion_for_Unpaired_Test_Time_Out-of-distribution_Text-Image_to_Video_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Pan_Reasoning_Diffusion_for_CVPR_2026_supplemental.zip | null | null | @InProceedings{Pan_2026_CVPR,
author = {Pan, Zirui and Wang, Xin and Zhang, Yipeng and Chen, Hong and Zheng, Kecheng and Zhu, Wenwu},
title = {Reasoning Diffusion for Unpaired Test Time Out-of-distribution Text-Image to Video Generation},
booktitle = {Proceedings of the IEEE/CVF Conference on Compute... | Text-image to video generation aims to synthesize a video conditioned on the given text-image inputs. Nevertheless, existing methods generally assume that the semantic information carried in the input text and image tends to be perfectly paired and temporally aligned, occurring simultaneously in the generated video. As... |
Li_Disentangling_to_Re-couple_Resolving_the_Similarity-Controllability_Paradox_in_Subject-Driven_Text-to-Image_CVPR_2026_paper | Disentangling to Re-couple: Resolving the Similarity-Controllability Paradox in Subject-Driven Text-to-Image Generation | [
"Shuang Li",
"Chao Deng",
"Hang Chen",
"Liqun Liu",
"Zhenyu Hu",
"Te Cao",
"Mengge Xue",
"Yuan Chen",
"Peng Shu",
"Huan Yu",
"Jie Jiang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Li_Disentangling_to_Re-couple_Resolving_the_Similarity-Controllability_Paradox_in_Subject-Driven_Text-to-Image_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Li_Disentangling_to_Re-couple_Resolving_the_Similarity-Controllability_Paradox_in_Subject-Driven_Text-to-Image_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Li_Disentangling_to_Re-couple_CVPR_2026_supplemental.zip | 2604.00849 | cvf | @InProceedings{Li_2026_CVPR,
author = {Li, Shuang and Deng, Chao and Chen, Hang and Liu, Liqun and Hu, Zhenyu and Cao, Te and Xue, Mengge and Chen, Yuan and Shu, Peng and Yu, Huan and Jiang, Jie},
title = {Disentangling to Re-couple: Resolving the Similarity-Controllability Paradox in Subject-Driven Text... | Subject-Driven Text-to-Image (T2I) Generation aims to preserve a subject's identity while editing its context based on a text prompt. A core challenge in this task is the "similarity-controllability paradox", where enhancing textual control often degrades the subject's fidelity, and vice-versa. We argue this paradox st... |
Zhou_Generalizing_Visual_Geometry_Priors_to_Sparse_Gaussian_Occupancy_Prediction_CVPR_2026_paper | Generalizing Visual Geometry Priors to Sparse Gaussian Occupancy Prediction | [
"Changqing Zhou",
"Yueru Luo",
"Changhao Chen"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Zhou_Generalizing_Visual_Geometry_Priors_to_Sparse_Gaussian_Occupancy_Prediction_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zhou_Generalizing_Visual_Geometry_Priors_to_Sparse_Gaussian_Occupancy_Prediction_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Zhou_Generalizing_Visual_Geometry_CVPR_2026_supplemental.pdf | 2602.21552 | cvf | @InProceedings{Zhou_2026_CVPR,
author = {Zhou, Changqing and Luo, Yueru and Chen, Changhao},
title = {Generalizing Visual Geometry Priors to Sparse Gaussian Occupancy Prediction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {... | Accurate 3D scene understanding is essential for embodied intelligence, with occupancy prediction emerging as a key task for reasoning about both objects and free space. Existing approaches largely rely on depth priors (e.g., DepthAnything) but make only limited use of 3D cues, restricting performance and generalizatio... |
Li_ReWeaver_Towards_Simulation-Ready_and_Topology-Accurate_Garment_Reconstruction_CVPR_2026_paper | ReWeaver: Towards Simulation-Ready and Topology-Accurate Garment Reconstruction | [
"Ming Li",
"Hui Shan",
"Kai Zheng",
"Chentao Shen",
"Siyu Liu",
"Yanwei Fu",
"Zhen Chen",
"Xiangru Huang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Li_ReWeaver_Towards_Simulation-Ready_and_Topology-Accurate_Garment_Reconstruction_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Li_ReWeaver_Towards_Simulation-Ready_and_Topology-Accurate_Garment_Reconstruction_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Li_ReWeaver_Towards_Simulation-Ready_CVPR_2026_supplemental.zip | 2601.16672 | cvf | @InProceedings{Li_2026_CVPR,
author = {Li, Ming and Shan, Hui and Zheng, Kai and Shen, Chentao and Liu, Siyu and Fu, Yanwei and Chen, Zhen and Huang, Xiangru},
title = {ReWeaver: Towards Simulation-Ready and Topology-Accurate Garment Reconstruction},
booktitle = {Proceedings of the IEEE/CVF Conferenc... | High-quality 3D garment reconstruction plays a crucial role in mitigating the sim-to-real gap in applications such as digital avatars, virtual try-on and robotic manipulation. However, existing garment reconstruction methods typically rely on unstructured representations, such as 3D Gaussian Splats, struggling to provi... |
Wu_Scan_Clusters_Not_Pixels_A_Cluster-Centric_Paradigm_for_Efficient_Ultra-high-definition_CVPR_2026_paper | Scan Clusters, Not Pixels: A Cluster-Centric Paradigm for Efficient Ultra-high-definition Image Restoration | [
"Chen Wu",
"Ling Wang",
"Zhuoran Zheng",
"Yuning Cui",
"Zhixiong Yang",
"Xiangyu Chen",
"Yue Zhang",
"Weidong Jiang",
"Jingyuan Xia"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Wu_Scan_Clusters_Not_Pixels_A_Cluster-Centric_Paradigm_for_Efficient_Ultra-high-definition_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Wu_Scan_Clusters_Not_Pixels_A_Cluster-Centric_Paradigm_for_Efficient_Ultra-high-definition_CVPR_2026_paper.pdf | null | 2602.21917 | cvf | @InProceedings{Wu_2026_CVPR,
author = {Wu, Chen and Wang, Ling and Zheng, Zhuoran and Cui, Yuning and Yang, Zhixiong and Chen, Xiangyu and Zhang, Yue and Jiang, Weidong and Xia, Jingyuan},
title = {Scan Clusters, Not Pixels: A Cluster-Centric Paradigm for Efficient Ultra-high-definition Image Restoration... | Ultra-High-Definition (UHD) image restoration is trapped in a scalability crisis: existing models, bound to pixel-wise operations, demand unsustainable computation. While state space models (SSMs) like Mamba promise linear complexity, their pixel-serial scanning remains a fundamental bottleneck for the millions of pixe... |
Alansari_SPARROW_Learning_Spatial_Precision_and_Temporal_Referential_Consistency_in_Pixel-Grounded_CVPR_2026_paper | SPARROW: Learning Spatial Precision and Temporal Referential Consistency in Pixel-Grounded Video MLLMs | [
"Mohamad Alansari",
"Naufal Suryanto",
"Divya Velayudhan",
"Sajid Javed",
"Naoufel Werghi",
"Muzammal Naseer"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Alansari_SPARROW_Learning_Spatial_Precision_and_Temporal_Referential_Consistency_in_Pixel-Grounded_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Alansari_SPARROW_Learning_Spatial_Precision_and_Temporal_Referential_Consistency_in_Pixel-Grounded_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Alansari_SPARROW_Learning_Spatial_CVPR_2026_supplemental.pdf | 2603.12382 | cvf | @InProceedings{Alansari_2026_CVPR,
author = {Alansari, Mohamad and Suryanto, Naufal and Velayudhan, Divya and Javed, Sajid and Werghi, Naoufel and Naseer, Muzammal},
title = {SPARROW: Learning Spatial Precision and Temporal Referential Consistency in Pixel-Grounded Video MLLMs},
booktitle = {Proceedi... | Multimodal large language models (MLLMs) have advanced from image-level reasoning to pixel-level grounding, but extending these capabilities to videos remains challenging as models must achieve spatial precision and temporally consistent reference tracking. Existing video MLLMs often rely on a static segmentation token... |
Maqbool_PETAR_Localized_Findings_Generation_with_Mask-Aware_Vision-Language_Modeling_for_PET_CVPR_2026_paper | PETAR: Localized Findings Generation with Mask-Aware Vision-Language Modeling for PET Automated Reporting | [
"Danyal Maqbool",
"Changhee Lee",
"Zachary Huemann",
"Samuel D. Church",
"Matthew E. Larson",
"Scott B. Perlman",
"Tomas A. Romero",
"Joshua D. Warner",
"Meghan Lubner",
"Xin Tie",
"Jameson Merkow",
"Junjie Hu",
"Steve Y. Cho",
"Tyler J. Bradshaw"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Maqbool_PETAR_Localized_Findings_Generation_with_Mask-Aware_Vision-Language_Modeling_for_PET_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Maqbool_PETAR_Localized_Findings_Generation_with_Mask-Aware_Vision-Language_Modeling_for_PET_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Maqbool_PETAR_Localized_Findings_CVPR_2026_supplemental.pdf | 2510.27680 | cvf | @InProceedings{Maqbool_2026_CVPR,
author = {Maqbool, Danyal and Lee, Changhee and Huemann, Zachary and Church, Samuel D. and Larson, Matthew E. and Perlman, Scott B. and Romero, Tomas A. and Warner, Joshua D. and Lubner, Meghan and Tie, Xin and Merkow, Jameson and Hu, Junjie and Cho, Steve Y. and Bradshaw, Tyler... | Generating automated reports for 3D positron emission tomography (PET) is an important and challenging task in medical imaging. PET plays a vital role in oncology, but automating report generation is difficult due to the complexity of whole-body 3D volumes, the wide range of potential clinical findings, and the limited... |
Li_PROMPTMINER_Black-Box_Prompt_Stealing_against_Text-to-Image_Generative_Models_via_Reinforcement_CVPR_2026_paper | PROMPTMINER: Black-Box Prompt Stealing against Text-to-Image Generative Models via Reinforcement Learning and VLM-Guided Optimization | [
"Mingzhe Li",
"Renhao Zhang",
"Zhiyang Wen",
"Siqi Pan",
"Bruno Castro da Silva",
"Juan Zhai",
"Shiqing Ma"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Li_PROMPTMINER_Black-Box_Prompt_Stealing_against_Text-to-Image_Generative_Models_via_Reinforcement_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Li_PROMPTMINER_Black-Box_Prompt_Stealing_against_Text-to-Image_Generative_Models_via_Reinforcement_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Li_PROMPTMINER_Black-Box_Prompt_CVPR_2026_supplemental.zip | 2511.22119 | title_judge | @InProceedings{Li_2026_CVPR,
author = {Li, Mingzhe and Zhang, Renhao and Wen, Zhiyang and Pan, Siqi and da Silva, Bruno Castro and Zhai, Juan and Ma, Shiqing},
title = {PROMPTMINER: Black-Box Prompt Stealing against Text-to-Image Generative Models via Reinforcement Learning and VLM-Guided Optimization},
... | Text-to-image (T2I) generative models such as Stable Diffusion and FLUX can synthesize realistic, high-quality images directly from textual prompts. The resulting image quality depends critically on well-crafted prompts that specify both subjects and stylistic modifiers, which have become valuable digital assets. Howev... |
Xu_Iterative_Closed-Loop_Motion_Synthesis_for_Scaling_the_Capabilities_of_Humanoid_CVPR_2026_paper | Iterative Closed-Loop Motion Synthesis for Scaling the Capabilities of Humanoid Control | [
"Weisheng Xu",
"Qiwei Wu",
"Jiaxi Zhang",
"Jing Tan",
"Yangfan Li",
"Yuetong Fang",
"Jiaqi Xiong",
"Kai Wu",
"Rong Ou",
"Renjing Xu"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Xu_Iterative_Closed-Loop_Motion_Synthesis_for_Scaling_the_Capabilities_of_Humanoid_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Xu_Iterative_Closed-Loop_Motion_Synthesis_for_Scaling_the_Capabilities_of_Humanoid_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Xu_Iterative_Closed-Loop_Motion_CVPR_2026_supplemental.zip | 2602.21599 | cvf | @InProceedings{Xu_2026_CVPR,
author = {Xu, Weisheng and Wu, Qiwei and Zhang, Jiaxi and Tan, Jing and Li, Yangfan and Fang, Yuetong and Xiong, Jiaqi and Wu, Kai and Ou, Rong and Xu, Renjing},
title = {Iterative Closed-Loop Motion Synthesis for Scaling the Capabilities of Humanoid Control},
booktitle =... | Physics-based humanoid control relies on training with motion datasets that have diverse data distributions. However, the fixed difficulty distribution of datasets limits the performance ceiling of the trained control policies. Additionally, the method of acquiring high-quality data through professional motion capture ... |
Wu_Editprint_General_Digital_Image_Forensics_via_Editing_Fingerprint_with_Self-Augmentation_CVPR_2026_paper | Editprint: General Digital Image Forensics via Editing Fingerprint with Self-Augmentation Training | [
"Haiwei Wu",
"Kemou Li",
"Yuanman Li",
"Jiantao Zhou"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Wu_Editprint_General_Digital_Image_Forensics_via_Editing_Fingerprint_with_Self-Augmentation_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Wu_Editprint_General_Digital_Image_Forensics_via_Editing_Fingerprint_with_Self-Augmentation_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Wu_Editprint_General_Digital_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Wu_2026_CVPR,
author = {Wu, Haiwei and Li, Kemou and Li, Yuanman and Zhou, Jiantao},
title = {Editprint: General Digital Image Forensics via Editing Fingerprint with Self-Augmentation Training},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognit... | Digital image forensics can ensure information credibility in tasks like camera source identification (CSI), synthetic image detection (SID), and social network provenance (SNP). These tasks typically rely on image processing history clues left by in-camera operations, post-capture editing, or synthetic generation. How... |
Zhu_When_Lines_Meet_Textures_Spatial-Frequency_Aligned_Diffusion_Features_for_Cross-Sparsity_CVPR_2026_paper | When Lines Meet Textures: Spatial-Frequency Aligned Diffusion Features for Cross-Sparsity Correspondence | [
"Mingrui Zhu",
"Fengzhi Wang",
"Xin Wei",
"Jun Wang",
"Nannan Wang",
"Xinbo Gao"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Zhu_When_Lines_Meet_Textures_Spatial-Frequency_Aligned_Diffusion_Features_for_Cross-Sparsity_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zhu_When_Lines_Meet_Textures_Spatial-Frequency_Aligned_Diffusion_Features_for_Cross-Sparsity_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Zhu_When_Lines_Meet_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Zhu_2026_CVPR,
author = {Zhu, Mingrui and Wang, Fengzhi and Wei, Xin and Wang, Jun and Wang, Nannan and Gao, Xinbo},
title = {When Lines Meet Textures: Spatial-Frequency Aligned Diffusion Features for Cross-Sparsity Correspondence},
booktitle = {Proceedings of the IEEE/CVF Conference o... | Establishing accurate correspondence between sparse line representations and rich textured imagery remains a formidable challenge. While diffusion features excel in semantic correspondence, they struggle to bridge the fundamental gap between abstract sketches and texture-rich photographs. We identify two critical dispa... |
Cheng_Routing_on_Demand_DSNet_for_Efficient_Progressive_Point_Cloud_Denoising_CVPR_2026_paper | Routing on Demand: DSNet for Efficient Progressive Point Cloud Denoising | [
"Xiaoqian Cheng",
"Dong Xiao",
"Husen Li",
"Zheng Liu",
"Renjie Chen"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Cheng_Routing_on_Demand_DSNet_for_Efficient_Progressive_Point_Cloud_Denoising_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Cheng_Routing_on_Demand_DSNet_for_Efficient_Progressive_Point_Cloud_Denoising_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Cheng_Routing_on_Demand_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Cheng_2026_CVPR,
author = {Cheng, Xiaoqian and Xiao, Dong and Li, Husen and Liu, Zheng and Chen, Renjie},
title = {Routing on Demand: DSNet for Efficient Progressive Point Cloud Denoising},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | Point cloud denoising is a critical preprocessing step for enhancing the reliability and accuracy of 3D perception systems. Most existing progressive denoising methods rely on fixed iterative pipelines that process all regions uniformly, resulting in redundant computation and over-smoothing of geometric details when ha... |
Li_ElasticFormer_Detecting_Objects_in_HRW_Shots_via_Elastic_Computing_Vision_CVPR_2026_paper | ElasticFormer: Detecting Objects in HRW Shots via Elastic Computing Vision Transformer | [
"Wenxi Li",
"Jingchen Huang",
"Chenyang Lyu",
"Moran Liu",
"Haozhe Lin",
"Guiguang Ding",
"Yuchen Guo"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Li_ElasticFormer_Detecting_Objects_in_HRW_Shots_via_Elastic_Computing_Vision_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Li_ElasticFormer_Detecting_Objects_in_HRW_Shots_via_Elastic_Computing_Vision_CVPR_2026_paper.pdf | null | null | null | @InProceedings{Li_2026_CVPR,
author = {Li, Wenxi and Huang, Jingchen and Lyu, Chenyang and Liu, Moran and Lin, Haozhe and Ding, Guiguang and Guo, Yuchen},
title = {ElasticFormer: Detecting Objects in HRW Shots via Elastic Computing Vision Transformer},
booktitle = {Proceedings of the IEEE/CVF Confere... | Recent advances in gigapixel-level imaging have brought High-Resolution Wide shots to the forefront of research. However, these images present significant challenges: extreme sparsity of foreground, gigapixel-level resolutions and diverse target counts. This makes traditional close-up detectors inaccurate and slow as ... |
Chavan_S2D_Selective_Spectral_Decay_for_Quantization-Friendly_Conditioning_of_Neural_Activations_CVPR_2026_paper | S2D: Selective Spectral Decay for Quantization-Friendly Conditioning of Neural Activations | [
"Arnav Chavan",
"Nahush Lele",
"Udbhav Bamba",
"Sankalp Dayal",
"Aditi Raghunathan",
"Deepak Gupta"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Chavan_S2D_Selective_Spectral_Decay_for_Quantization-Friendly_Conditioning_of_Neural_Activations_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Chavan_S2D_Selective_Spectral_Decay_for_Quantization-Friendly_Conditioning_of_Neural_Activations_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Chavan_S2D_Selective_Spectral_CVPR_2026_supplemental.pdf | 2602.14432 | cvf | @InProceedings{Chavan_2026_CVPR,
author = {Chavan, Arnav and Lele, Nahush and Bamba, Udbhav and Dayal, Sankalp and Raghunathan, Aditi and Gupta, Deepak},
title = {S2D: Selective Spectral Decay for Quantization-Friendly Conditioning of Neural Activations},
booktitle = {Proceedings of the IEEE/CVF Conf... | Activation outliers in large-scale transformer models pose a fundamental challenge to model quantization, creating excessively large ranges that cause severe accuracy drops during quantization. We empirically observe that outlier severity intensifies with pre-training scale (e.g., progressing from CLIP to the more exte... |
Vyas_Pushing_the_Frontier_of_Audiovisual_Perception_with_Large-Scale_Multimodal_Correspondence_CVPR_2026_paper | Pushing the Frontier of Audiovisual Perception with Large-Scale Multimodal Correspondence Learning | [
"Apoorv Vyas",
"Heng-Jui Chang",
"Cheng-Fu Yang",
"Po-Yao Huang",
"Luya Gao",
"Julius Richter",
"Sanyuan Chen",
"Matthew Le",
"Piotr Dollár",
"Christoph Feichtenhofer",
"Ann Lee",
"Wei-Ning Hsu"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Vyas_Pushing_the_Frontier_of_Audiovisual_Perception_with_Large-Scale_Multimodal_Correspondence_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Vyas_Pushing_the_Frontier_of_Audiovisual_Perception_with_Large-Scale_Multimodal_Correspondence_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Vyas_Pushing_the_Frontier_CVPR_2026_supplemental.pdf | 2512.19687 | cvf | @InProceedings{Vyas_2026_CVPR,
author = {Vyas, Apoorv and Chang, Heng-Jui and Yang, Cheng-Fu and Huang, Po-Yao and Gao, Luya and Richter, Julius and Chen, Sanyuan and Le, Matthew and Doll\'ar, Piotr and Feichtenhofer, Christoph and Lee, Ann and Hsu, Wei-Ning},
title = {Pushing the Frontier of Audiovisual... | We introduce Perception Encoder-Audiovisual, PE-AV, a new family of encoders for audio and video understanding trained with scaled contrastive learning. Building on PE, PE-AV makes several key contributions to extend representations to audio, and natively support joint embeddings across audio-video, audio-text, and vid... |
Kim_PhysGaia_A_Physics-aware_Benchmark_with_Multi-Body_Interactions_for_Dynamic_Novel_CVPR_2026_paper | PhysGaia: A Physics-aware Benchmark with Multi-Body Interactions for Dynamic Novel View Synthesis | [
"Mijeong Kim",
"Gunhee Kim",
"Jungyoon Choi",
"Wonjae Roh",
"Bohyung Han"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Kim_PhysGaia_A_Physics-aware_Benchmark_with_Multi-Body_Interactions_for_Dynamic_Novel_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Kim_PhysGaia_A_Physics-aware_Benchmark_with_Multi-Body_Interactions_for_Dynamic_Novel_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Kim_PhysGaia_A_Physics-aware_CVPR_2026_supplemental.pdf | 2506.02794 | cvf | @InProceedings{Kim_2026_CVPR,
author = {Kim, Mijeong and Kim, Gunhee and Choi, Jungyoon and Roh, Wonjae and Han, Bohyung},
title = {PhysGaia: A Physics-aware Benchmark with Multi-Body Interactions for Dynamic Novel View Synthesis},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visio... | We introduce PhysGaia, a novel physics-aware benchmark for Dynamic Novel View Synthesis (DyNVS) that encompasses both structured objects and unstructured physical phenomena. While existing datasets primarily focus on photorealistic appearance, PhysGaia is specifically designed to support physics-consistent dynamic reco... |
Deng_NS-Diff_Fluid_Navier-Stokes_Guided_Video_Diffusion_via_Reinforcement_Learning_CVPR_2026_paper | NS-Diff: Fluid Navier-Stokes Guided Video Diffusion via Reinforcement Learning | [
"Zijun Deng",
"Yuxin Peng"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Deng_NS-Diff_Fluid_Navier-Stokes_Guided_Video_Diffusion_via_Reinforcement_Learning_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Deng_NS-Diff_Fluid_Navier-Stokes_Guided_Video_Diffusion_via_Reinforcement_Learning_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Deng_NS-Diff_Fluid_Navier-Stokes_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Deng_2026_CVPR,
author = {Deng, Zijun and Peng, Yuxin},
title = {NS-Diff: Fluid Navier-Stokes Guided Video Diffusion via Reinforcement Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | While recent video generation models achieve impressive visual quality, generating physically plausible videos remains challenging, especially for fluid dynamics and rigid-body motions. To address this, we present **NS-Diff**, a physics-guided reinforcement learning framework for video diffusion. First, we design a noi... |
Liu_CaliTex_Geometry-Calibrated_Attention_for_View-Coherent_3D_Texture_Generation_CVPR_2026_paper | CaliTex: Geometry-Calibrated Attention for View-Coherent 3D Texture Generation | [
"Chenyu Liu",
"Hongze Chen",
"Jingzhi Bao",
"Lingting Zhu",
"Runze Zhang",
"Weikai Chen",
"Zeyu Hu",
"Yingda Yin",
"Keyang Luo",
"Xin Wang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Liu_CaliTex_Geometry-Calibrated_Attention_for_View-Coherent_3D_Texture_Generation_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Liu_CaliTex_Geometry-Calibrated_Attention_for_View-Coherent_3D_Texture_Generation_CVPR_2026_paper.pdf | null | 2511.21309 | cvf | @InProceedings{Liu_2026_CVPR,
author = {Liu, Chenyu and Chen, Hongze and Bao, Jingzhi and Zhu, Lingting and Zhang, Runze and Chen, Weikai and Hu, Zeyu and Yin, Yingda and Luo, Keyang and Wang, Xin},
title = {CaliTex: Geometry-Calibrated Attention for View-Coherent 3D Texture Generation},
booktitle = ... | Despite major advances brought by diffusion-based models, current 3D texture generation systems remain hindered by cross-view inconsistency -- textures that appear convincing from one viewpoint often fail to align across others. We find that this issue arises from attention ambiguity, where unstructured full attention ... |
Li_DynamicTree_Interactive_Real_Tree_Animation_via_Sparse_Voxel_Spectrum_CVPR_2026_paper | DynamicTree: Interactive Real Tree Animation via Sparse Voxel Spectrum | [
"Yaokun Li",
"Lihe Ding",
"Xiao Chen",
"Guang Tan",
"Tianfan Xue"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Li_DynamicTree_Interactive_Real_Tree_Animation_via_Sparse_Voxel_Spectrum_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Li_DynamicTree_Interactive_Real_Tree_Animation_via_Sparse_Voxel_Spectrum_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Li_DynamicTree_Interactive_Real_CVPR_2026_supplemental.pdf | 2510.22213 | cvf | @InProceedings{Li_2026_CVPR,
author = {Li, Yaokun and Ding, Lihe and Chen, Xiao and Tan, Guang and Xue, Tianfan},
title = {DynamicTree: Interactive Real Tree Animation via Sparse Voxel Spectrum},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Generating dynamic and interactive 3D trees has wide applications in virtual reality, games, and world simulation. However, existing methods still face various challenges in generating structurally consistent and realistic 4D motion for complex real trees. In this paper, we propose DynamicTree, the first framework that... |
Ikeda_Ghost-FWL_A_Large-Scale_Full-Waveform_LiDAR_Dataset_for_Ghost_Detection_and_CVPR_2026_paper | Ghost-FWL: A Large-Scale Full-Waveform LiDAR Dataset for Ghost Detection and Removal | [
"Kazuma Ikeda",
"Ryosei Hara",
"Rokuto Nagata",
"Ozora Sako",
"Zihao Ding",
"Takahiro Kado",
"Ibuki Fujioka",
"Taro Beppu",
"Mariko Isogawa",
"Kentaro Yoshioka"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Ikeda_Ghost-FWL_A_Large-Scale_Full-Waveform_LiDAR_Dataset_for_Ghost_Detection_and_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Ikeda_Ghost-FWL_A_Large-Scale_Full-Waveform_LiDAR_Dataset_for_Ghost_Detection_and_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Ikeda_Ghost-FWL_A_Large-Scale_CVPR_2026_supplemental.pdf | 2603.28224 | cvf | @InProceedings{Ikeda_2026_CVPR,
author = {Ikeda, Kazuma and Hara, Ryosei and Nagata, Rokuto and Sako, Ozora and Ding, Zihao and Kado, Takahiro and Fujioka, Ibuki and Beppu, Taro and Isogawa, Mariko and Yoshioka, Kentaro},
title = {Ghost-FWL: A Large-Scale Full-Waveform LiDAR Dataset for Ghost Detection a... | LiDAR has become an essential sensing modality in autonomous driving, robotics, and smart-city applications. However, ghost points (or ghost), which are false reflections caused by multi-path laser returns from glass and reflective surfaces, severely degrade 3D mapping and localization accuracy. Prior ghost removal rel... |
Lu_YoCity_Personalized_and_Boundless_3D_Realistic_City_Scene_Generation_via_CVPR_2026_paper | Yo'City: Personalized and Boundless 3D Realistic City Scene Generation via Self-Critic Expansion | [
"Keyang Lu",
"Sifan Zhou",
"Hongbin Xu",
"Gang Xu",
"Zhifei Yang",
"Yikai Wang",
"Zhen Xiao",
"Jieyi Long",
"Ming Li"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Lu_YoCity_Personalized_and_Boundless_3D_Realistic_City_Scene_Generation_via_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Lu_YoCity_Personalized_and_Boundless_3D_Realistic_City_Scene_Generation_via_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Lu_YoCity_Personalized_and_CVPR_2026_supplemental.pdf | 2511.18734 | title_snapshot | @InProceedings{Lu_2026_CVPR,
author = {Lu, Keyang and Zhou, Sifan and Xu, Hongbin and Xu, Gang and Yang, Zhifei and Wang, Yikai and Xiao, Zhen and Long, Jieyi and Li, Ming},
title = {Yo'City: Personalized and Boundless 3D Realistic City Scene Generation via Self-Critic Expansion},
booktitle = {Procee... | Realistic 3D city generation is fundamental to a wide range of applications, including virtual reality and digital twins. However, most existing methods rely on training a single diffusion model, which limits their ability to generate personalized and boundless city-scale scenes. In this paper, we present Yo'City, a no... |
Koebler_LVLM-Aided_Alignment_of_Task-Specific_Vision_Models_CVPR_2026_paper | LVLM-Aided Alignment of Task-Specific Vision Models | [
"Alexander Koebler",
"Lukas Kuhn",
"Ingo Thon",
"Florian Buettner"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Koebler_LVLM-Aided_Alignment_of_Task-Specific_Vision_Models_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Koebler_LVLM-Aided_Alignment_of_Task-Specific_Vision_Models_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Koebler_LVLM-Aided_Alignment_of_CVPR_2026_supplemental.pdf | 2512.21985 | cvf | @InProceedings{Koebler_2026_CVPR,
author = {Koebler, Alexander and Kuhn, Lukas and Thon, Ingo and Buettner, Florian},
title = {LVLM-Aided Alignment of Task-Specific Vision Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = ... | In high-stakes domains, small task-specific vision models are crucial due to their low computational requirements and the availability of numerous methods to explain their results. However, these explanations often reveal that the models do not align well with human domain knowledge, relying instead on spurious correla... |
Heo_Detecting_Unknown_Objects_via_Energy-based_Separation_for_Open_World_Object_CVPR_2026_paper | Detecting Unknown Objects via Energy-based Separation for Open World Object Detection | [
"Jun-Woo Heo",
"Keonhee Park",
"Gyeong-Moon Park"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Heo_Detecting_Unknown_Objects_via_Energy-based_Separation_for_Open_World_Object_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Heo_Detecting_Unknown_Objects_via_Energy-based_Separation_for_Open_World_Object_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Heo_Detecting_Unknown_Objects_CVPR_2026_supplemental.pdf | 2603.29954 | cvf | @InProceedings{Heo_2026_CVPR,
author = {Heo, Jun-Woo and Park, Keonhee and Park, Gyeong-Moon},
title = {Detecting Unknown Objects via Energy-based Separation for Open World Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
m... | In this work, we tackle the problem of Open World Object Detection (OWOD). This challenging scenario requires the detector to incrementally learn to classify known objects without forgetting while identifying unknown objects without supervision. Previous OWOD methods have enhanced the unknown discovery process and empl... |
Zhu_EpiAgent_An_Agent-Centric_System_for_Ancient_Inscription_Restoration_CVPR_2026_paper | EpiAgent: An Agent-Centric System for Ancient Inscription Restoration | [
"Shipeng Zhu",
"Ang Chen",
"Na Nie",
"Pengfei Fang",
"Min-Ling Zhang",
"Hui Xue"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Zhu_EpiAgent_An_Agent-Centric_System_for_Ancient_Inscription_Restoration_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zhu_EpiAgent_An_Agent-Centric_System_for_Ancient_Inscription_Restoration_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Zhu_EpiAgent_An_Agent-Centric_CVPR_2026_supplemental.pdf | 2604.09367 | cvf | @InProceedings{Zhu_2026_CVPR,
author = {Zhu, Shipeng and Chen, Ang and Nie, Na and Fang, Pengfei and Zhang, Min-Ling and Xue, Hui},
title = {EpiAgent: An Agent-Centric System for Ancient Inscription Restoration},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog... | Ancient inscriptions, as repositories of cultural memory, have suffered from centuries of environmental and human-induced degradation. Restoring their intertwined visual and textual integrity poses one of the most demanding challenges in digital heritage preservation. However, existing AI-based approaches often rely on... |
Li_Unified_Latent_Space_for_Understanding_and_Generation_via_Semantic_Auto-encoder_CVPR_2026_paper | Unified Latent Space for Understanding and Generation via Semantic Auto-encoder | [
"Xiaojie Li",
"Yang Zhao",
"Ming Li",
"Yancheng Zhang",
"Zonglin Lyu",
"Yunpeng Chen",
"Rui Wang",
"Daquan Zhou"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Li_Unified_Latent_Space_for_Understanding_and_Generation_via_Semantic_Auto-encoder_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Li_Unified_Latent_Space_for_Understanding_and_Generation_via_Semantic_Auto-encoder_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Li_Unified_Latent_Space_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Li_2026_CVPR,
author = {Li, Xiaojie and Zhao, Yang and Li, Ming and Zhang, Yancheng and Lyu, Zonglin and Chen, Yunpeng and Wang, Rui and Zhou, Daquan},
title = {Unified Latent Space for Understanding and Generation via Semantic Auto-encoder},
booktitle = {Proceedings of the IEEE/CVF Co... | Latent generative modeling has emerged as the dominant paradigm for Diffusion Transformers (DiT), where a pretrained autoencoder compresses image pixels into a latent space to facilitate the diffusion process. Recently, the use of semantic encoders within autoencoders (AEs) has gained attention, yet their influence on ... |
Ko_Diffusion-Based_sRGB_Real_Noise_Generation_via_Prompt-Driven_Noise_Representation_Learning_CVPR_2026_paper | Diffusion-Based sRGB Real Noise Generation via Prompt-Driven Noise Representation Learning | [
"Jaekyun Ko",
"Dongjin Kim",
"Soomin Lee",
"Guanghui Wang",
"Tae Hyun Kim"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Ko_Diffusion-Based_sRGB_Real_Noise_Generation_via_Prompt-Driven_Noise_Representation_Learning_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Ko_Diffusion-Based_sRGB_Real_Noise_Generation_via_Prompt-Driven_Noise_Representation_Learning_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Ko_Diffusion-Based_sRGB_Real_CVPR_2026_supplemental.pdf | 2603.04870 | cvf | @InProceedings{Ko_2026_CVPR,
author = {Ko, Jaekyun and Kim, Dongjin and Lee, Soomin and Wang, Guanghui and Kim, Tae Hyun},
title = {Diffusion-Based sRGB Real Noise Generation via Prompt-Driven Noise Representation Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and P... | Denoising in the sRGB image space is challenging due to large noise variability. Although end-to-end methods perform well, their effectiveness in real-world scenarios is limited by the scarcity of real noisy-clean image pairs, which are expensive and difficult to collect. To address this limitation, several generative ... |
Fan_DiGraphHal-Bench_Evaluating_Multimodal_Large_Language_Models_on_Complex_Directed_Graphs_CVPR_2026_paper | DiGraphHal-Bench: Evaluating Multimodal Large Language Models on Complex Directed Graphs | [
"Yixin Fan",
"Zhao He",
"Yuxin Hou",
"Changhua Zhou",
"Zihao Liu",
"Peng Wang",
"Chenglong Lu",
"Xu Zhang",
"Wei Wang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Fan_DiGraphHal-Bench_Evaluating_Multimodal_Large_Language_Models_on_Complex_Directed_Graphs_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Fan_DiGraphHal-Bench_Evaluating_Multimodal_Large_Language_Models_on_Complex_Directed_Graphs_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Fan_DiGraphHal-Bench_Evaluating_Multimodal_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Fan_2026_CVPR,
author = {Fan, Yixin and He, Zhao and Hou, Yuxin and Zhou, Changhua and Liu, Zihao and Wang, Peng and Lu, Chenglong and Zhang, Xu and Wang, Wei},
title = {DiGraphHal-Bench: Evaluating Multimodal Large Language Models on Complex Directed Graphs},
booktitle = {Proceedings ... | While prior research on Multimodal Large Language Model (MLLM) hallucinations has primarily examined cross-modal inconsistencies in natural images, hallucination over complex graph structures remains underexplored.Concurrently, there is a lack of robust evaluation for fine-grained reasoning integrating structural, visu... |
Shan_Talking_Together_Synthesizing_Co-Located_3D_Conversations_from_Audio_CVPR_2026_paper | Talking Together: Synthesizing Co-Located 3D Conversations from Audio | [
"Mengyi Shan",
"Shouchieh Chang",
"Ziqian Bai",
"Shichen Liu",
"Yinda Zhang",
"Luchuan Song",
"Rohit Pandey",
"Sean Fanello",
"Zeng Huang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Shan_Talking_Together_Synthesizing_Co-Located_3D_Conversations_from_Audio_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Shan_Talking_Together_Synthesizing_Co-Located_3D_Conversations_from_Audio_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Shan_Talking_Together_Synthesizing_CVPR_2026_supplemental.zip | 2603.08674 | cvf | @InProceedings{Shan_2026_CVPR,
author = {Shan, Mengyi and Chang, Shouchieh and Bai, Ziqian and Liu, Shichen and Zhang, Yinda and Song, Luchuan and Pandey, Rohit and Fanello, Sean and Huang, Zeng},
title = {Talking Together: Synthesizing Co-Located 3D Conversations from Audio},
booktitle = {Proceeding... | We tackle the challenging task of generating complete 3D facial animations for two interacting, co-located participants from a mixed audio stream. While existing methods often produce disembodied "talking heads" akin to a video conference call, our work is the first to explicitly model the dynamic 3D spatial relationsh... |
Cheng_MERG3R_A_Divide-and-Conquer_Approach_to_Large-Scale_Neural_Visual_Geometry_CVPR_2026_paper | MERG3R: A Divide-and-Conquer Approach to Large-Scale Neural Visual Geometry | [
"Leo Kaixuan Cheng",
"Abdus Shaikh",
"Ruofan Liang",
"Zhijie Wu",
"Yushi Guan",
"Nandita Vijaykumar"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Cheng_MERG3R_A_Divide-and-Conquer_Approach_to_Large-Scale_Neural_Visual_Geometry_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Cheng_MERG3R_A_Divide-and-Conquer_Approach_to_Large-Scale_Neural_Visual_Geometry_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Cheng_MERG3R_A_Divide-and-Conquer_CVPR_2026_supplemental.pdf | 2603.02351 | cvf | @InProceedings{Cheng_2026_CVPR,
author = {Cheng, Leo Kaixuan and Shaikh, Abdus and Liang, Ruofan and Wu, Zhijie and Guan, Yushi and Vijaykumar, Nandita},
title = {MERG3R: A Divide-and-Conquer Approach to Large-Scale Neural Visual Geometry},
booktitle = {Proceedings of the IEEE/CVF Conference on Compu... | Recent advancements in neural visual geometry, including transformer-based models such as VGGT and Pi3, have achieved impressive accuracy on 3D reconstruction tasks. However, their reliance on full attention makes them fundamentally limited by GPU memory capacity, preventing them from scaling to large, unordered image ... |
Zhu_Alert-CLIP_Abnormality-aware_Latent-Enhanced_Representation_Tuning_of_CLIP_for_Video_Anomaly_CVPR_2026_paper | Alert-CLIP: Abnormality-aware Latent-Enhanced Representation Tuning of CLIP for Video Anomaly Detection | [
"Yiyan Zhu",
"Menghao Zhang",
"Haifeng Sun",
"Pengfei Ren",
"Xianao Chu",
"Chenye Xu",
"Hong Tan",
"Jinghan Wang",
"Qi Qi",
"Jingyu Wang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Zhu_Alert-CLIP_Abnormality-aware_Latent-Enhanced_Representation_Tuning_of_CLIP_for_Video_Anomaly_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zhu_Alert-CLIP_Abnormality-aware_Latent-Enhanced_Representation_Tuning_of_CLIP_for_Video_Anomaly_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Zhu_Alert-CLIP_Abnormality-aware_Latent-Enhanced_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Zhu_2026_CVPR,
author = {Zhu, Yiyan and Zhang, Menghao and Sun, Haifeng and Ren, Pengfei and Chu, Xianao and Xu, Chenye and Tan, Hong and Wang, Jinghan and Qi, Qi and Wang, Jingyu},
title = {Alert-CLIP: Abnormality-aware Latent-Enhanced Representation Tuning of CLIP for Video Anomaly Detec... | With the rise of pre-trained vision-language models such as CLIP, performing video anomaly detection (VAD) through cross-modal reasoning has become an emerging trend. However, we observe that CLIP still suffers from weak abnormality awareness: normal and abnormal descriptions are highly entangled in the text embedding ... |
Fang_Dropping_Anchor_and_Spherical_Harmonics_for_Sparse-view_Gaussian_Splatting_CVPR_2026_paper | Dropping Anchor and Spherical Harmonics for Sparse-view Gaussian Splatting | [
"Shuangkang Fang",
"I-Chao Shen",
"Xuanyang Zhang",
"Zesheng Wang",
"Yufeng Wang",
"Wenrui Ding",
"Gang YU",
"Takeo Igarashi"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Fang_Dropping_Anchor_and_Spherical_Harmonics_for_Sparse-view_Gaussian_Splatting_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Fang_Dropping_Anchor_and_Spherical_Harmonics_for_Sparse-view_Gaussian_Splatting_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Fang_Dropping_Anchor_and_CVPR_2026_supplemental.pdf | 2602.20933 | cvf | @InProceedings{Fang_2026_CVPR,
author = {Fang, Shuangkang and Shen, I-Chao and Zhang, Xuanyang and Wang, Zesheng and Wang, Yufeng and Ding, Wenrui and YU, Gang and Igarashi, Takeo},
title = {Dropping Anchor and Spherical Harmonics for Sparse-view Gaussian Splatting},
booktitle = {Proceedings of the I... | Recent 3D Gaussian Splatting (3DGS) dropout methods address overfitting under sparse-view conditions by randomly nullifying Gaussian opacities. However, we identify a neighbor compensation effect in these approaches: dropped Gaussians are often compensated by their neighbors, weakening the intended regularization. More... |
Lee_Neural-Centric_Video_Processing_Pipeline_for_Unified_Multi-Task_Inference_CVPR_2026_paper | Neural-Centric Video Processing Pipeline for Unified Multi-Task Inference | [
"Seyeon Lee",
"Juncheol Ye",
"Jaehong Kim",
"Dongsu Han"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Lee_Neural-Centric_Video_Processing_Pipeline_for_Unified_Multi-Task_Inference_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Lee_Neural-Centric_Video_Processing_Pipeline_for_Unified_Multi-Task_Inference_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Lee_Neural-Centric_Video_Processing_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Lee_2026_CVPR,
author = {Lee, Seyeon and Ye, Juncheol and Kim, Jaehong and Han, Dongsu},
title = {Neural-Centric Video Processing Pipeline for Unified Multi-Task Inference},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mont... | Videos are increasingly used as inputs to machine learning systems, where repeated decoding and processing across diverse downstream tasks dominate computational cost. However, existing video pipelines remain inefficient. Traditional codecs such as H.264 and H.265 are optimized for human perception and require full pix... |
Wei_MICo-150K_A_Comprehensive_Dataset_Advancing_Multi-Image_Composition_CVPR_2026_paper | MICo-150K: A Comprehensive Dataset Advancing Multi-Image Composition | [
"Xinyu Wei",
"Kangrui Cen",
"Hongyang Wei",
"Zhen Guo",
"Bairui Li",
"Zeqing Wang",
"Jinrui Zhang",
"Lei Zhang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Wei_MICo-150K_A_Comprehensive_Dataset_Advancing_Multi-Image_Composition_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Wei_MICo-150K_A_Comprehensive_Dataset_Advancing_Multi-Image_Composition_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Wei_MICo-150K_A_Comprehensive_CVPR_2026_supplemental.pdf | 2512.07348 | cvf | @InProceedings{Wei_2026_CVPR,
author = {Wei, Xinyu and Cen, Kangrui and Wei, Hongyang and Guo, Zhen and Li, Bairui and Wang, Zeqing and Zhang, Jinrui and Zhang, Lei},
title = {MICo-150K: A Comprehensive Dataset Advancing Multi-Image Composition},
booktitle = {Proceedings of the IEEE/CVF Conference on... | In controllable image generation, synthesizing coherent and consistent images from multiple reference inputs, i.e., **Multi-Image Composition** (MICo), remains a challenging problem, partly hindered by the lack of high-quality training data.To bridge this gap, we conduct a systematic study of MICo, categorizing it into... |
Jia_Scalable_Multi-View_Subspace_Clustering_with_Tensorized_Anchor_Guidance_CVPR_2026_paper | Scalable Multi-View Subspace Clustering with Tensorized Anchor Guidance | [
"Miao Jia",
"Xingchen Hu",
"Jiyuan Liu",
"Siwei Wang",
"Min Wang",
"Zijian Chen"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Jia_Scalable_Multi-View_Subspace_Clustering_with_Tensorized_Anchor_Guidance_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Jia_Scalable_Multi-View_Subspace_Clustering_with_Tensorized_Anchor_Guidance_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Jia_Scalable_Multi-View_Subspace_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Jia_2026_CVPR,
author = {Jia, Miao and Hu, Xingchen and Liu, Jiyuan and Wang, Siwei and Wang, Min and Chen, Zijian},
title = {Scalable Multi-View Subspace Clustering with Tensorized Anchor Guidance},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Rec... | Anchor-based multi-view clustering methods have gained significant attention for their effectiveness in handling large-scale datasets in recent years. The performance of these methods is highly dependent on anchor quality. However, current methods neglect the interactive relationships among cross-view anchors, failing ... |
Zhang_MedTVT-R1_A_Multimodal_LLM_Empowering_Medical_Reasoning_and_Diagnosis_CVPR_2026_paper | MedTVT-R1: A Multimodal LLM Empowering Medical Reasoning and Diagnosis | [
"Yuting Zhang",
"Kaishen Yuan",
"Hao Lu",
"Yutao Yue",
"Jintai Chen",
"Kaishun Wu"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Zhang_MedTVT-R1_A_Multimodal_LLM_Empowering_Medical_Reasoning_and_Diagnosis_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zhang_MedTVT-R1_A_Multimodal_LLM_Empowering_Medical_Reasoning_and_Diagnosis_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Zhang_MedTVT-R1_A_Multimodal_CVPR_2026_supplemental.pdf | 2506.18512 | cvf | @InProceedings{Zhang_2026_CVPR,
author = {Zhang, Yuting and Yuan, Kaishen and Lu, Hao and Yue, Yutao and Chen, Jintai and Wu, Kaishun},
title = {MedTVT-R1: A Multimodal LLM Empowering Medical Reasoning and Diagnosis},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ... | Accurate and interpretable multi-disease diagnosis remains a critical challenge in medical research, particularly when leveraging heterogeneous multimodal medical data. Current approaches often rely on single-modal data, limiting their ability to comprehensively understand complex diseases. To address this, we propose ... |
Fan_Good_Can_Sometimes_be_Bad_A_Unified_Attack_against_3D_CVPR_2026_paper | Good Can Sometimes be Bad: A Unified Attack against 3D Point Cloud Classifier by a Flexible Isotropic Resampling | [
"Linkun Fan",
"Jiahao Zhang",
"Juntao Zhang",
"Lei Zhang",
"Fazhi He",
"Daojun Han"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Fan_Good_Can_Sometimes_be_Bad_A_Unified_Attack_against_3D_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Fan_Good_Can_Sometimes_be_Bad_A_Unified_Attack_against_3D_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Fan_Good_Can_Sometimes_CVPR_2026_supplemental.pdf | null | null | @InProceedings{Fan_2026_CVPR,
author = {Fan, Linkun and Zhang, Jiahao and Zhang, Juntao and Zhang, Lei and He, Fazhi and Han, Daojun},
title = {Good Can Sometimes be Bad: A Unified Attack against 3D Point Cloud Classifier by a Flexible Isotropic Resampling},
booktitle = {Proceedings of the IEEE/CVF C... | To ensure the robustness of 3D point cloud Deep Neural Network(3D DNN), 3D adversarial attack targeting the inference stage and backdoor attack targeting the training stage are well studied. The success of both attacks usually requires a specified permissions that attacker must have. However, the obtainable permissions... |
Gu_CryoHype_Reconstructing_a_thousand_cryo-EM_structures_with_transformer-based_hypernetworks_CVPR_2026_paper | CryoHype: Reconstructing a thousand cryo-EM structures with transformer-based hypernetworks | [
"Jeffrey Gu",
"Minkyu Jeon",
"Ambri Ma",
"Serena Yeung-Levy",
"Ellen D. Zhong"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Gu_CryoHype_Reconstructing_a_thousand_cryo-EM_structures_with_transformer-based_hypernetworks_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Gu_CryoHype_Reconstructing_a_thousand_cryo-EM_structures_with_transformer-based_hypernetworks_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Gu_CryoHype_Reconstructing_a_CVPR_2026_supplemental.pdf | 2512.06332 | cvf | @InProceedings{Gu_2026_CVPR,
author = {Gu, Jeffrey and Jeon, Minkyu and Ma, Ambri and Yeung-Levy, Serena and Zhong, Ellen D.},
title = {CryoHype: Reconstructing a thousand cryo-EM structures with transformer-based hypernetworks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision ... | Cryo-electron microscopy (cryo-EM) is an indispensable technique for determining the 3D structures of dynamic biomolecular complexes. While typically applied to image a single molecular species, cryo-EM holds great potential for structure determination of many targets simultaneously in a high-throughput fashion. Howeve... |
Choi_Image-Guided_Geometric_Stylization_of_3D_Meshes_CVPR_2026_paper | Image-Guided Geometric Stylization of 3D Meshes | [
"Changwoon Choi",
"Hyunsoo Lee",
"Clément Jambon",
"Yael Vinker",
"Young Min Kim"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Choi_Image-Guided_Geometric_Stylization_of_3D_Meshes_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Choi_Image-Guided_Geometric_Stylization_of_3D_Meshes_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Choi_Image-Guided_Geometric_Stylization_CVPR_2026_supplemental.pdf | 2604.07795 | cvf | @InProceedings{Choi_2026_CVPR,
author = {Choi, Changwoon and Lee, Hyunsoo and Jambon, Cl\'ement and Vinker, Yael and Kim, Young Min},
title = {Image-Guided Geometric Stylization of 3D Meshes},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Recent generative models can create visually plausible 3D representations of objects. However, the generation process often allows for implicit control signals, such as contextual descriptions, and rarely supports bold geometric distortions beyond existing data distributions. We propose a geometric stylization framewor... |
Shen_PHANTOM_Physics-Infused_Video_Generation_via_Joint_Modeling_of_Visual_and_CVPR_2026_paper | PHANTOM: Physics-Infused Video Generation via Joint Modeling of Visual and Latent Physical Dynamics | [
"Ying Shen",
"Jerry Xiong",
"Tianjiao Yu",
"Ismini Lourentzou"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Shen_PHANTOM_Physics-Infused_Video_Generation_via_Joint_Modeling_of_Visual_and_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Shen_PHANTOM_Physics-Infused_Video_Generation_via_Joint_Modeling_of_Visual_and_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Shen_PHANTOM_Physics-Infused_Video_CVPR_2026_supplemental.pdf | 2604.08503 | cvf | @InProceedings{Shen_2026_CVPR,
author = {Shen, Ying and Xiong, Jerry and Yu, Tianjiao and Lourentzou, Ismini},
title = {PHANTOM: Physics-Infused Video Generation via Joint Modeling of Visual and Latent Physical Dynamics},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patt... | Recent advances in generative video modeling, driven by large-scale datasets and powerful architectures, have yielded remarkable visual realism. However, emerging evidence suggests that simply scaling data and model size does not endow these systems with an understanding of the underlying physical laws that govern real... |
Jeong_Learning_Multi-View_Spatial_Reasoning_from_Cross-View_Relations_CVPR_2026_paper | Learning Multi-View Spatial Reasoning from Cross-View Relations | [
"Suchae Jeong",
"Jaehwi Song",
"Haeone Lee",
"Hanna Kim",
"Jian Kim",
"Dongjun Lee",
"Dong Kyu Shin",
"Changyeon Kim",
"Dongyoon Hahm",
"Woogyeol Jin",
"Juheon Choi",
"Kimin Lee"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Jeong_Learning_Multi-View_Spatial_Reasoning_from_Cross-View_Relations_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Jeong_Learning_Multi-View_Spatial_Reasoning_from_Cross-View_Relations_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Jeong_Learning_Multi-View_Spatial_CVPR_2026_supplemental.zip | 2603.27967 | cvf | @InProceedings{Jeong_2026_CVPR,
author = {Jeong, Suchae and Song, Jaehwi and Lee, Haeone and Kim, Hanna and Kim, Jian and Lee, Dongjun and Shin, Dong Kyu and Kim, Changyeon and Hahm, Dongyoon and Jin, Woogyeol and Choi, Juheon and Lee, Kimin},
title = {Learning Multi-View Spatial Reasoning from Cross-Vie... | Vision-language models (VLMs) have achieved impressive results on single-view vision tasks, but lack the multi-view spatial reasoning capabilities essential for embodied AI systems to understand 3D environments and manipulate objects across different viewpoints. In this work, we introduce Cross-View Relations (XVR), a ... |
Tan_UCAN_Unified_Convolutional_Attention_Network_for_Expansive_Receptive_Fields_in_CVPR_2026_paper | UCAN: Unified Convolutional Attention Network for Expansive Receptive Fields in Lightweight Super-Resolution | [
"Cao Thien Tan",
"Phan Thi Thu Trang",
"Do Nghiem Duc",
"Ho Ngoc Anh",
"Hanyang Zhuang",
"Nguyen Duc Dung"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Tan_UCAN_Unified_Convolutional_Attention_Network_for_Expansive_Receptive_Fields_in_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Tan_UCAN_Unified_Convolutional_Attention_Network_for_Expansive_Receptive_Fields_in_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Tan_UCAN_Unified_Convolutional_CVPR_2026_supplemental.pdf | 2603.11680 | cvf | @InProceedings{Tan_2026_CVPR,
author = {Tan, Cao Thien and Trang, Phan Thi Thu and Duc, Do Nghiem and Anh, Ho Ngoc and Zhuang, Hanyang and Dung, Nguyen Duc},
title = {UCAN: Unified Convolutional Attention Network for Expansive Receptive Fields in Lightweight Super-Resolution},
booktitle = {Proceeding... | Hybrid CNN-Transformer architectures achieve strong results in image super-resolution, but scaling attention windows or convolution kernels significantly increases computational cost, limiting deployment on resource-constrained devices. We present UCAN, a lightweight network that unifies convolution and attention to ex... |
Choi_Splat-Based_Metal_Artifact_Reduction_in_Cone-Beam_CT_via_Compact_Attenuation_CVPR_2026_paper | Splat-Based Metal Artifact Reduction in Cone-Beam CT via Compact Attenuation Modeling | [
"Kiseok Choi",
"Jaemin Cho",
"Inchul Kim",
"Min H. Kim"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Choi_Splat-Based_Metal_Artifact_Reduction_in_Cone-Beam_CT_via_Compact_Attenuation_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Choi_Splat-Based_Metal_Artifact_Reduction_in_Cone-Beam_CT_via_Compact_Attenuation_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Choi_Splat-Based_Metal_Artifact_CVPR_2026_supplemental.zip | null | null | @InProceedings{Choi_2026_CVPR,
author = {Choi, Kiseok and Cho, Jaemin and Kim, Inchul and Kim, Min H.},
title = {Splat-Based Metal Artifact Reduction in Cone-Beam CT via Compact Attenuation Modeling},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR... | X-ray computed tomography (CT) suffers from severe metal artifacts when high-attenuation objects such as dental fillings or orthopedic implants are present. These artifacts originate from the polychromatic nature of X-rays, where attenuation varies strongly with photon energy and material composition, breaking the mono... |
Kumar_Measuring_the_UnFaithfulness_of_Concept-Based_Explanations_CVPR_2026_paper | Measuring the (Un)Faithfulness of Concept-Based Explanations | [
"Shubham Kumar",
"Narendra Ahuja"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Kumar_Measuring_the_UnFaithfulness_of_Concept-Based_Explanations_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Kumar_Measuring_the_UnFaithfulness_of_Concept-Based_Explanations_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Kumar_Measuring_the_UnFaithfulness_CVPR_2026_supplemental.pdf | 2504.10833 | cvf | @InProceedings{Kumar_2026_CVPR,
author = {Kumar, Shubham and Ahuja, Narendra},
title = {Measuring the (Un)Faithfulness of Concept-Based Explanations},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2026}... | Deep vision models perform input-output computations that are hard to interpret. Concept-based explanation methods (CBEMs) increase interpretability by re-expressing parts of the model with human-understandable semantic units, or concepts. Checking if the derived explanations are faithful--that is, they represent the m... |
Luo_Preserving_Source_Video_Realism_High-Fidelity_Face_Swapping_for_Cinematic_Quality_CVPR_2026_paper | Preserving Source Video Realism: High-Fidelity Face Swapping for Cinematic Quality | [
"Zekai Luo",
"Zongze Du",
"Zhouhang Zhu",
"Hao Zhong",
"Muzhi Zhu",
"Wen Wang",
"Yuling Xi",
"Chenchen Jing",
"Hao Chen",
"Chunhua Shen"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Luo_Preserving_Source_Video_Realism_High-Fidelity_Face_Swapping_for_Cinematic_Quality_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Luo_Preserving_Source_Video_Realism_High-Fidelity_Face_Swapping_for_Cinematic_Quality_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Luo_Preserving_Source_Video_CVPR_2026_supplemental.pdf | 2512.07951 | cvf | @InProceedings{Luo_2026_CVPR,
author = {Luo, Zekai and Du, Zongze and Zhu, Zhouhang and Zhong, Hao and Zhu, Muzhi and Wang, Wen and Xi, Yuling and Jing, Chenchen and Chen, Hao and Shen, Chunhua},
title = {Preserving Source Video Realism: High-Fidelity Face Swapping for Cinematic Quality},
booktitle =... | Video face swapping is crucial in film and entertainment production, where achieving high fidelity and temporal consistency over long and complex video sequences remains a significant challenge. Inspired by recent advances in reference-guided image editing, we explore whether rich visual attributes from source videos c... |
Stergiou_TRANSPORTER_Transferring_Visual_Semantics_from_VLM_Manifolds_CVPR_2026_paper | TRANSPORTER: Transferring Visual Semantics from VLM Manifolds | [
"Alexandros Stergiou"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Stergiou_TRANSPORTER_Transferring_Visual_Semantics_from_VLM_Manifolds_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Stergiou_TRANSPORTER_Transferring_Visual_Semantics_from_VLM_Manifolds_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Stergiou_TRANSPORTER_Transferring_Visual_CVPR_2026_supplemental.pdf | 2511.18359 | cvf | @InProceedings{Stergiou_2026_CVPR,
author = {Stergiou, Alexandros},
title = {TRANSPORTER: Transferring Visual Semantics from VLM Manifolds},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2026},
page... | How do video understanding models acquire their answers? Although current Vision Language Models (VLMs) reason over complex scenes with diverse objects, action performances, and scene dynamics, understanding and controlling their internal processes remains an open challenge. Motivated by recent advancements in text-to-... |
Nguyen_BALM_A_Model-Agnostic_Framework_for_Balanced_Multimodal_Learning_under_Imbalanced_CVPR_2026_paper | BALM: A Model-Agnostic Framework for Balanced Multimodal Learning under Imbalanced Missing Rates | [
"Phuong-Anh Nguyen",
"Tien Anh Pham",
"Duc-Trong Le",
"Cam-Van Thi Nguyen"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Nguyen_BALM_A_Model-Agnostic_Framework_for_Balanced_Multimodal_Learning_under_Imbalanced_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Nguyen_BALM_A_Model-Agnostic_Framework_for_Balanced_Multimodal_Learning_under_Imbalanced_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Nguyen_BALM_A_Model-Agnostic_CVPR_2026_supplemental.pdf | 2603.19718 | cvf | @InProceedings{Nguyen_2026_CVPR,
author = {Nguyen, Phuong-Anh and Pham, Tien Anh and Le, Duc-Trong and Nguyen, Cam-Van Thi},
title = {BALM: A Model-Agnostic Framework for Balanced Multimodal Learning under Imbalanced Missing Rates},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visi... | Learning from multiple modalities often suffers from imbalance, where information-rich modalities dominate optimization while weaker or partially missing modalities contribute less. This imbalance becomes severe in realistic settings with imbalanced missing modalities (IMR), where each modality is absent with different... |
Liang_PerformRecast_Expression_and_Head_Pose_Disentanglement_for_Portrait_Video_Editing_CVPR_2026_paper | PerformRecast: Expression and Head Pose Disentanglement for Portrait Video Editing | [
"Jiadong Liang",
"Bojun Xiong",
"Jie Tian",
"Hua Li",
"Xiao Long",
"Yong Zheng",
"Huan Fu"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Liang_PerformRecast_Expression_and_Head_Pose_Disentanglement_for_Portrait_Video_Editing_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Liang_PerformRecast_Expression_and_Head_Pose_Disentanglement_for_Portrait_Video_Editing_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Liang_PerformRecast_Expression_and_CVPR_2026_supplemental.zip | 2603.19731 | cvf | @InProceedings{Liang_2026_CVPR,
author = {Liang, Jiadong and Xiong, Bojun and Tian, Jie and Li, Hua and Long, Xiao and Zheng, Yong and Fu, Huan},
title = {PerformRecast: Expression and Head Pose Disentanglement for Portrait Video Editing},
booktitle = {Proceedings of the IEEE/CVF Conference on Comput... | This paper primarily investigates the task of expression-only portrait video performance editing based on a driving video, which plays a crucial role in animation and film industries. Most existing research mainly focuses on portrait animation, which aims to animate a static portrait image according to the facial motio... |
Mazur_4D_Primitive-Mache_Glueing_Primitives_for_Persistent_4D_Scene_Reconstruction_CVPR_2026_paper | 4D Primitive-Mache: Glueing Primitives for Persistent 4D Scene Reconstruction | [
"Kirill Mazur",
"Marwan Taher",
"Andrew J. Davison"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Mazur_4D_Primitive-Mache_Glueing_Primitives_for_Persistent_4D_Scene_Reconstruction_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Mazur_4D_Primitive-Mache_Glueing_Primitives_for_Persistent_4D_Scene_Reconstruction_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Mazur_4D_Primitive-Mache_Glueing_CVPR_2026_supplemental.zip | 2512.16564 | title_snapshot | @InProceedings{Mazur_2026_CVPR,
author = {Mazur, Kirill and Taher, Marwan and Davison, Andrew J.},
title = {4D Primitive-Mache: Glueing Primitives for Persistent 4D Scene Reconstruction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month... | We present 4DPM, a dynamic reconstruction system that receives a casual monocular RGB video as input, and outputs a complete and persistent reconstruction of the scene. In other words, we reconstruct not only the currently visible parts of the scene, but also all previously viewed parts, which enables replaying the co... |
Kwon_Zero-Shot_Reconstruction_of_Animatable_3D_Avatars_with_Cloth_Dynamics_from_CVPR_2026_paper | Zero-Shot Reconstruction of Animatable 3D Avatars with Cloth Dynamics from a Single Image | [
"Joohyun Kwon",
"Geonhee Sim",
"Gyeongsik Moon"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Kwon_Zero-Shot_Reconstruction_of_Animatable_3D_Avatars_with_Cloth_Dynamics_from_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Kwon_Zero-Shot_Reconstruction_of_Animatable_3D_Avatars_with_Cloth_Dynamics_from_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Kwon_Zero-Shot_Reconstruction_of_CVPR_2026_supplemental.pdf | 2603.14772 | cvf | @InProceedings{Kwon_2026_CVPR,
author = {Kwon, Joohyun and Sim, Geonhee and Moon, Gyeongsik},
title = {Zero-Shot Reconstruction of Animatable 3D Avatars with Cloth Dynamics from a Single Image},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Existing single-image 3D human avatar methods primarily rely on rigid joint transformations, limiting their ability to model realistic cloth dynamics. We present DynaAvatar, a zero-shot framework that reconstructs animatable 3D human avatars with motion-dependent cloth dynamics from a single image. Trained on large-sca... |
Rosh_HandDreamer_Zero-Shot_Text_to_3D_Hand_Model_Generation_using_Corrective_CVPR_2026_paper | HandDreamer: Zero-Shot Text to 3D Hand Model Generation using Corrective Hand Shape Guidance | [
"Green Rosh",
"Prateek Kukreja",
"Vishakha SR",
"Pawan Prasad B H"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Rosh_HandDreamer_Zero-Shot_Text_to_3D_Hand_Model_Generation_using_Corrective_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Rosh_HandDreamer_Zero-Shot_Text_to_3D_Hand_Model_Generation_using_Corrective_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Rosh_HandDreamer_Zero-Shot_Text_CVPR_2026_supplemental.zip | 2604.04425 | cvf | @InProceedings{Rosh_2026_CVPR,
author = {Rosh, Green and Kukreja, Prateek and Vishakha, SR and H, Pawan Prasad B},
title = {HandDreamer: Zero-Shot Text to 3D Hand Model Generation using Corrective Hand Shape Guidance},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern... | The emergence of virtual reality has necessitated the generation of detailed and customizable 3D hand models for interaction in the virtual world. However, the current methods for 3D hand model generation are both expensive and cumbersome, offering very little customizability to the users. While recent advancements in ... |
Zheng_Learning_Latent_Proxies_for_Controllable_Single-Image_Relighting_CVPR_2026_paper | Learning Latent Proxies for Controllable Single-Image Relighting | [
"Haoze Zheng",
"Zihao Wang",
"Xianfeng Wu",
"Yajing Bai",
"Yexin Liu",
"Yun Li",
"Xiaogang Xu",
"Harry Yang"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Zheng_Learning_Latent_Proxies_for_Controllable_Single-Image_Relighting_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zheng_Learning_Latent_Proxies_for_Controllable_Single-Image_Relighting_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Zheng_Learning_Latent_Proxies_CVPR_2026_supplemental.pdf | 2603.15555 | cvf | @InProceedings{Zheng_2026_CVPR,
author = {Zheng, Haoze and Wang, Zihao and Wu, Xianfeng and Bai, Yajing and Liu, Yexin and Li, Yun and Xu, Xiaogang and Yang, Harry},
title = {Learning Latent Proxies for Controllable Single-Image Relighting},
booktitle = {Proceedings of the IEEE/CVF Conference on Comp... | Single-image relighting is highly under-constrained: small illumination changes can produce large, nonlinear variations in shading, shadows, and specularities, while geometry and materials remain unobserved. Existing diffusion-based approaches either rely on intrinsic- or G-buffer-based pipelines that require dense and... |
Punnappurath_Edit-aware_RAW_reconstruction_CVPR_2026_paper | Edit-aware RAW reconstruction | [
"Abhijith Punnappurath",
"Luxi Zhao",
"Ke Zhao",
"Hue Nguyen",
"Radek Grzeszczuk",
"Michael S. Brown"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Punnappurath_Edit-aware_RAW_reconstruction_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Punnappurath_Edit-aware_RAW_reconstruction_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Punnappurath_Edit-aware_RAW_reconstruction_CVPR_2026_supplemental.pdf | 2512.05859 | cvf | @InProceedings{Punnappurath_2026_CVPR,
author = {Punnappurath, Abhijith and Zhao, Luxi and Zhao, Ke and Nguyen, Hue and Grzeszczuk, Radek and Brown, Michael S.},
title = {Edit-aware RAW reconstruction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CV... | Users frequently edit camera images post-capture to achieve their preferred photofinishing style. While editing in the RAW domain provides greater accuracy and flexibility, most edits are performed on the camera's display-referred output (e.g., 8-bit sRGB JPEG) since RAW images are rarely stored. Existing RAW reconstru... |
Zhang_View-Aware_Semantic_Alignment_for_Aerial-Ground_Person_Re-Identification_CVPR_2026_paper | View-Aware Semantic Alignment for Aerial-Ground Person Re-Identification | [
"Quan Zhang",
"Zeqiang Cai",
"Peiming Zhao",
"Jingze Wu",
"Cailun Wu",
"Hongbo Chen",
"Jianhuang Lai"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Zhang_View-Aware_Semantic_Alignment_for_Aerial-Ground_Person_Re-Identification_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zhang_View-Aware_Semantic_Alignment_for_Aerial-Ground_Person_Re-Identification_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Zhang_View-Aware_Semantic_Alignment_CVPR_2026_supplemental.pdf | 2605.18192 | cvf | @InProceedings{Zhang_2026_CVPR,
author = {Zhang, Quan and Cai, Zeqiang and Zhao, Peiming and Wu, Jingze and Wu, Cailun and Chen, Hongbo and Lai, Jianhuang},
title = {View-Aware Semantic Alignment for Aerial-Ground Person Re-Identification},
booktitle = {Proceedings of the IEEE/CVF Conference on Compu... | Aerial-Ground Person Re-Identification (AGPReID) remains highly challenging due to drastic viewpoint variations between drones and fixed cameras. Existing methods typically follow a view-invariant paradigm, aligning shared features across views to achieve robustness. However, view-invariant inherently enforces part-lev... |
Zhao_ChangeBridge_Spatiotemporal_Image_Generation_with_Multimodal_Controls_for_Remote_Senisng_CVPR_2026_paper | ChangeBridge: Spatiotemporal Image Generation with Multimodal Controls for Remote Senisng | [
"Zhenghui Zhao",
"Chen Wu",
"Xiangyong Cao",
"Di Wang",
"Hongruixuan Chen",
"Datao Tang",
"Liangpei Zhang",
"Zhuo Zheng"
] | https://openaccess.thecvf.com/content/CVPR2026/html/Zhao_ChangeBridge_Spatiotemporal_Image_Generation_with_Multimodal_Controls_for_Remote_Senisng_CVPR_2026_paper.html | https://openaccess.thecvf.com/content/CVPR2026/papers/Zhao_ChangeBridge_Spatiotemporal_Image_Generation_with_Multimodal_Controls_for_Remote_Senisng_CVPR_2026_paper.pdf | https://openaccess.thecvf.com/content/CVPR2026/supplemental/Zhao_ChangeBridge_Spatiotemporal_Image_CVPR_2026_supplemental.pdf | 2507.04678 | title_judge | @InProceedings{Zhao_2026_CVPR,
author = {Zhao, Zhenghui and Wu, Chen and Cao, Xiangyong and Wang, Di and Chen, Hongruixuan and Tang, Datao and Zhang, Liangpei and Zheng, Zhuo},
title = {ChangeBridge: Spatiotemporal Image Generation with Multimodal Controls for Remote Senisng},
booktitle = {Proceeding... | Spatiotemporal image generation is a highly meaningful task, which can generate future scenes conditioned on given observations. However, existing change generation methods can only handle event-driven changes (e.g., new buildings) and fail to model cross-temporal variations (e.g., seasonal shifts). In this work, we pr... |
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