PHUMA: Physically Reliable Humanoid Locomotion Dataset

This repository does NOT contain the complete PHUMA dataset.

Due to licensing restrictions, motions derived from LAFAN1 and LocoMuJoCo are excluded from this release. To obtain the full PHUMA dataset, please follow the instructions in our GitHub repository.

This repository provides physically reliable humanoid locomotion dataset, PHUMA.

PHUMA leverages large-scale human motion data while overcoming physical artifacts through careful data curation and physics-constrained retargeting to create a high-quality humanoid locomotion dataset.

For detailed results, implementation notes, and videos, please visit our paper, project page and GitHub repository.

Download and Setup

The dataset is provided as a compressed file. To use it:

# Download data.zip from this repository
# Then extract it:
unzip data.zip

This will create a data/ directory with all the motion data.

Dataset Structure

The dataset contains retargeted data for 2 different humanoids:

data/
├── g1/          # Humanoid configuration g1
└── h1_2/        # Humanoid configuration h1_2

Data Format

Each .npy file in the dataset follows a consistent structure:

{
    'root_trans': (num_frames, 3),      # Root translation (x, y, z)
    'root_ori': (num_frames, 4),        # Root orientation quaternion (x, y, z, w)
    'dof_pos': (num_frames, num_dof),   # Degrees of freedom positions for all joints
    'fps': fps                          # Frame rate (frames per second)
}

Field Descriptions

• root_trans: Root joint translation in 3D space (x, y, z) for each frame
• root_ori: Root joint orientation as quaternion (x, y, z, w) for each frame
• dof_pos: Joint positions for all degrees of freedom across frames
• fps: Frame rate of the motion sequence

Citation

If you find this dataset useful in your research, please cite our paper:

@article{lee2025phuma,
    title={PHUMA: Physically-Grounded Humanoid Locomotion Dataset}, 
    author={Kyungmin Lee and Sibeen Kim and Youngdo Lee and Minho Park and Hyunseung Kim and Dongyoon Hwang and Donghu Kim and Hojoon Lee and Jaegul Choo},
    journal={arXiv preprint arXiv:2510.26236},
    year={2025}
}