The code for IJCAI 2023 paper "Physics-Guided Human Motion Capture with Pose Probability Modeling"
Jingyi Ju, Buzhen Huang, Chen Zhu, Zhihao Li, Yangang Wang
[Paper]
Create conda environment and install dependencies.
conda create -n Physics-Guided-Mocap python=3.8
conda install pytorch torchvision torchaudio pytorch-cuda=11.1 -c pytorch -c nvidia # install pytorch
pip install -r requirements.txt
- Due to the licenses, please download SMPL model file here.
- The Mujoco environment is built into the project path(./mujoco_py) to avoid cumbersome environment configuration. We recommend to download mujoco-py-1.50.1.0 and mjpro150 and activation key for win10/win11.
Finally put these data following the directory structure as below:
${ROOT}
|-- assets
|-- mujoco_models
|-- bigfoot_template.pkl
|-- bigfoot_template_v1.pkl
|-- data
|-- mujoco
|-- mujoco-py-1.50.1.0
|-- mjpro150
|-- mjkey.txt
|-- sample_data
|-- amass_copycat_occlusion.pkl
|-- amass_copycat_take5_test_small.pkl
|-- standing_neutral.pkl
|-- smpl
|-- SMPL_NEUTRAL.pkl
|-- SMPL_FEMALE.pkl
|-- SMPL_MALE.pkl
|-- init_data.pkl
|-- iter_19000.p
|-- J_regressor_h36m.npy
|-- J_regressor_halpe.npy
|-- J_regressor_lsp.npy
- Reconstruct physically-plausible human motions with physics-guided diffusion framework.
python demo.py
- Release trained model
- Release training code
If you find this code useful for your research, please consider citing the paper.
@inproceedings{ijcai2023p105,
title = {Physics-Guided Human Motion Capture with Pose Probability Modeling},
author = {Ju, Jingyi and Huang, Buzhen and Zhu, Chen and Li, Zhihao and Wang, Yangang},
booktitle = {International Joint Conference on Artificial Intelligence, IJCAI},
pages = {947--955},
year = {2023},
}
Some of the code is based on the following works. We gratefully appreciate the impact they have on our work.