The code for 3DV 2021 paper "Dynamic Multi-Person Mesh Recovery From Uncalibrated Multi-View Cameras"
Buzhen Huang, Yuan Shu, Tianshu Zhang, Yangang Wang
[Paper] [Video]
Windows or Linux, Python3.7
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
pip install -r requirements.txt
Step1:
Download the official SMPL model from SMPLify website and put it in models/smpl. (see models/smpl/readme.txt)
Step2:
Download the test data and trained motion prior from Google Drive or Baidu Netdisk (extraction code [jomn]) and put them in data.
Step3:
Run
python main.py --config cfg_files/fit_smpl.yamlYou can visualize the motions and cameras in optimization with the command:
python main.py --config cfg_files/fit_smpl.yaml --visualize true
The code can also be used for motion capture with known cameras:
python main.py --config cfg_files/fit_smpl.yaml --opt_cam falseThe fitted results will be saved in output.
You can visualize the estimated extrinsic camera parameters by running:
python viz_cameras.py
If you find this code useful for your research, please consider citing the paper.
@article{huang2023simultaneously,
title={Simultaneously Recovering Multi-Person Meshes and Multi-View Cameras with Human Semantics},
author={Huang, Buzhen and Ju, Jingyi and Shu, Yuan and Wang, Yangang},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
year={2023},
publisher={IEEE}
}
@inproceedings{huang2021dynamic,
title={Dynamic Multi-Person Mesh Recovery From Uncalibrated Multi-View Cameras},
author={Buzhen Huang and Yuan Shu and Tianshu Zhang and Yangang Wang},
year={2021},
booktitle={3DV},
}
Some of the code are based on the following works. We gratefully appreciate the impact it has on our work.
SMPLify-x
SPIN
EasyMocap
MvSMPLfitting
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent