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