yan0116/SMPL_Humanoid_offline_dataset
UniPhys Dataset: Offline Dataset for Physics-based Character Control This dataset is part of the UniPhys, 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 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… See the full description on the dataset page: https://huggingface.co/datasets/yan0116/SMPL_Humanoid_offline_dataset.
UniPhys Dataset: Offline Dataset for Physics-based Character Control
This dataset is part of the UniPhys, 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 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 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_PATHBABEL state-action-text pairs
This is the training dataset used in UniPhys.
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.pklCitation
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}
}