| --- |
| license: cc-by-4.0 |
| task_categories: |
| - other |
| tags: |
| - egocentric |
| - pose-estimation |
| - RGBD |
| - SMPL |
| - motion-capture |
| - VR |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # EgoPoseVR Dataset |
|
|
| ## Overview |
|
|
| The **EgoPoseVR Dataset** is a large-scale synthetic dataset for egocentric full-body pose estimation in virtual reality. |
| It contains paired RGB-D observations, pose annotations, HMD tracking signals, and SMPL body parameters for temporally aligned motion clips. |
|
|
|  |
|
|
| - **Total samples**: 18,235 motion clips |
| - **Scenes**: 7 virtual scenes (`Scene0` - `Scene6`) |
| - **Train / Val / Test**: 14,702 / 1,827 / 1,706 |
| - **Data format**: `.npz` (NumPy compressed archives) |
|
|
| For more details, please visit the [Project Page](https://aplusx.github.io/EgoPoseVRWeb/) or check the [official repository](https://aplusx.github.io/EgoPoseVRWeb/). |
|
|
| --- |
|
|
| ## Data Sources |
|
|
| The motion data is derived from the [AMASS](https://amass.is.tue.mpg.de/) dataset. |
| In total, **2,344 motion sequences** are extracted. Each sequence folder corresponds to one continuous motion sequence, and each `.npz` file contains a 100-frame clip sampled from that sequence. |
|
|
| <p align="center"> |
| <strong>🎬 Dataset Video</strong><br> |
| <img src="assets/videos/Dataset.gif" alt="Dataset Video" width="500"> |
| </p> |
|
|
| --- |
|
|
| ## Directory Structure |
|
|
| ```text |
| EgoPoseVR_Dataset/ |
| ├── Scene0/ |
| ├── Scene1/ |
| ├── Scene2/ |
| ├── Scene3/ |
| ├── Scene4/ |
| ├── Scene5/ |
| ├── Scene6/ |
| │ └── AllDataPath_{Source}_{split}_{id}/ |
| │ └── {clip_id}.npz |
| ├── train_npz_paths.txt |
| ├── val_npz_paths.txt |
| ├── test_npz_paths.txt |
| └── all_npz_paths.txt |
| ``` |
|
|
| ## Citation |
|
|
| If you find our code or paper helps, please consider citing: |
|
|
| ```bibtex |
| @article{cheng2026egoposevr, |
| title={EgoPoseVR: Spatiotemporal Multi-Modal Reasoning for Egocentric Full-Body Pose in Virtual Reality}, |
| author={Cheng, Haojie and Ong, Shaun Jing Heng and Cai, Shaoyu and Koh, Aiden Tat Yang and Ouyang, Fuxi and Khoo, Eng Tat}, |
| journal={arXiv preprint arXiv:2602.05590}, |
| year={2026} |
| } |
| ``` |
|
|
