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) 69 = 23 joints × 3 DoF each
root_state	[T, 13]	Contains: - Position (0:3) - Quaternion (3:7) - Linear velocity (7:10) - 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.

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 and BABEL dataset.