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--- |
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license: mit |
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tags: |
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- SMPL-humanoid |
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- Mocap-tracking |
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- state-action-pairs |
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- imitation-learning |
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size_categories: |
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- 10K<n<100K |
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--- |
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# UniPhys Dataset: Offline Dataset for Physics-based Character Control |
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This dataset is part of the [UniPhys](https://wuyan01.github.io/uniphys-project/), enabling large-scale training of diffusion policies for physics-based humanoid control using SMPL-like characters. The state-action pairs are generated by the [PULSE](https://www.zhengyiluo.com/PULSE-Site/) motion tracking policy. |
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## Dataset Overview |
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* `amass_state-action-pairs`: state-action pairs for motion sequences from AMASS dataset (excluding infeasible motions) |
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* `babel_state-action-text-pairs`: Packaged AMASS motions with [BABEL](https://babel.is.tue.mpg.de/) frame-level text annotations. |
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### AMASS state-action pairs |
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#### Data Structure |
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For each sequence, the dataset contains: |
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| Field | Shape | Description | |
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|-------|-------|-------------| |
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| `body_pos` | `[T, 24, 3]` | Joint positions in global space | |
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| `dof_state` | `[T, 69, 2]` | Joint rotations (dim 0) and velocities (dim 1)<br>*69 = 23 joints × 3 DoF each* | |
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| `root_state` | `[T, 13]` | Contains:<br>- Position (0:3)<br>- Quaternion (3:7)<br>- Linear velocity (7:10)<br>- Angular velocity (10:13) | |
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| `action` | `[T, 69]` | Joint angle targets (23 joints × 3 DoF) | |
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| `pulse_z` | `[T, 32]` | Latent action space from PULSE policy | |
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| `is_succ` | `bool` | Tracking success flag (True/False) | |
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| `fps` | `int` | Frame rate (30 FPS) | |
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#### Visualization |
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To replay the sequence: |
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``` |
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python replay_amass_state_action_pairs.py --load_motion_path amass_state-action-pairs/$YOUR_FILE_PATH |
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``` |
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### BABEL state-action-text pairs |
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This is the training dataset used in [UniPhys](https://wuyan01.github.io/uniphys-project/). |
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#### Visualization |
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To replay the packaged offline BABEL dataset along with frame-level text annotation: |
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``` |
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python replay_babel_state_action_text_pairs.py --load_motion_path babel_state-action-text-pairs/babel_train.pkl |
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``` |
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## Citation |
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If using this dataset useful, please cite: |
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``` |
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@inproceedings{wu2025uniphys, |
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title={UniPhys: Unified Planner and Controller with Diffusion for Flexible Physics-Based Character Control}, |
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author={Wu, Yan and Karunratanakul, Korrawe and Luo, Zhengyi and Tang, Siyu}, |
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booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, |
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year={2025} |
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} |
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@inproceedings{ |
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luo2024universal, |
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title={Universal Humanoid Motion Representations for Physics-Based Control}, |
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author={Zhengyi Luo and Jinkun Cao and Josh Merel and Alexander Winkler and Jing Huang and Kris M. Kitani and Weipeng Xu}, |
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booktitle={The Twelfth International Conference on Learning Representations}, |
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year={2024}, |
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url={https://openreview.net/forum?id=OrOd8PxOO2} |
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} |
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``` |
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## License |
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Subject to original licenses of [AMASS](https://amass.is.tue.mpg.de/) and [BABEL](https://babel.is.tue.mpg.de/) dataset. |
