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README.md

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-license: apache-2.0
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+# EvHumanMotion
+
+[Dataset on HuggingFace 🤗](https://huggingface.co/datasets/potentialming/EvHumanMotion)
+
+**EvHumanMotion** is a real-world dataset captured using the DAVIS346 event camera, focusing on human motion under diverse and challenging scenarios. It is designed to support event-driven human animation research, especially under motion blur, low-light, and overexposure conditions. This dataset was introduced in the [EvAnimate](https://arxiv.org/abs/2503.18552) paper.
+
+## Dataset Structure
+
+The dataset is organized into two main parts:
+
+- **EvHumanMotion_aedat4**: Raw event streams in `.aedat4` format.
+- **EvHumanMotion_frame**: Frame-level event slices and RGB frames.
+
+Each is categorized into:
+- `indoor_day/`
+- `indoor_night_high_noise/`
+- `indoor_night_low_noise/`
+- `outdoor_day/`
+- `outdoor_night/`
+
+Each environment contains four scenarios:
+- `low_light/`
+- `motion_blur/`
+- `normal/`
+- `over_exposure/`
+
+Example path:
+
+```
+EvHumanMotion_frame/indoor_day/low_light/dvSave-2025_03_04_13_02_53/
+├── event_frames/
+│   ├── events_0000013494.png
+│   └── ...
+└── frames/
+    ├── frames_1741064573617350.png
+    └── ...
+```
+
+## Features
+
+- **Total sequences**: 113
+- **Participants**: 20 (10 male, 10 female)
+- **Duration**: ~10 seconds per video
+- **Frame rate**: 24 fps
+- **Scenarios**: Normal, Motion Blur, Overexposure, Low Light
+- **Modalities**: RGB + Event data (both `.aedat4` and frame-level)
+
+## Applications
+
+This dataset supports:
+- Event-to-video generation
+- Human animation in extreme conditions
+- Motion transfer with high temporal resolution
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("potentialming/EvHumanMotion")
+```
+
+## License
+
+Apache 2.0 License
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@article{qu2025evanimate,
+  title={EvAnimate: Event-conditioned Image-to-Video Generation for Human Animation},
+  author={Qu, Qiang and Li, Ming and Chen, Xiaoming and Liu, Tongliang},
+  journal={arXiv preprint arXiv:2503.18552},
+  year={2025}
+}
+```
+
+## Contact
+
+Dataset maintained by [Ming Li](https://huggingface.co/potentialming).

dataset_card.json

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+{'dataset_name': 'EvHumanMotion', 'pretty_name': 'EvHumanMotion', 'license': 'apache-2.0', 'language': ['en'], 'multilinguality': 'no', 'size_categories': ['10K<n<100K'], 'source_datasets': None, 'task_categories': ['video-to-video', 'event-to-video', 'human motion generation'], 'tags': ['event camera', 'video generation', 'human animation', 'motion transfer', 'real-world', 'low-light', 'motion-blur', 'diffusion'], 'dataset_info': {'features': {'event_stream': '.aedat4 format event file', 'event_frames': 'Sliced event frames in .png format', 'video_frames': 'RGB frame sequence', 'scenario': 'One of [normal, motion_blur, over_exposure, low_light]', 'environment': 'One of [indoor_day, indoor_night_high_noise, indoor_night_low_noise, outdoor_day, outdoor_night]'}, 'splits': {'train': 'custom split recommended', 'validation': 'custom split recommended', 'test': 'custom split recommended'}}, 'description': 'EvHumanMotion is a real-world dataset captured using DAVIS346 event camera, containing 113 sequences of human motion under various scenarios including motion blur, low light, and overexposure. It is designed for event-driven video generation and motion understanding. Each sequence includes RGB frames and aligned event data, in both .aedat4 and frame-level formats.', 'homepage': 'https://huggingface.co/datasets/potentialming/EvHumanMotion', 'citation': '@article{qu2025evanimate,\n  title={EvAnimate: Event-conditioned Image-to-Video Generation for Human Animation},\n  author={Qu, Qiang and Li, Ming and Chen, Xiaoming and Liu, Tongliang},\n  journal={arXiv preprint arXiv:2503.18552},\n  year={2025}\n}'}