| # EvHumanMotion | |
| [Dataset on HuggingFace π€](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/ | |
| βββ event_frames/ | |
| β βββ events_0000013494.png | |
| β βββ ... | |
| βββ frames/ | |
| βββ frames_1741064573617350.png | |
| βββ ... | |
| ``` | |
| ## 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). | |