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PyMAF-X

Introduction

We provide the config files for PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images.

@inproceedings{pymaf2021,
  title={PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop},
  author={Zhang, Hongwen and Tian, Yating and Zhou, Xinchi and Ouyang, Wanli and Liu, Yebin and Wang, Limin and Sun, Zhenan},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  year={2021}
}

@article{pymafx2022,
  title={PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images},
  author={Zhang, Hongwen and Tian, Yating and Zhang, Yuxiang and Li, Mengcheng and An, Liang and Sun, Zhenan and Liu, Yebin},
  journal={arXiv preprint arXiv:2207.06400},
  year={2022}
}

Notes

  • SMPL v1.0 is used in our experiments.

    • Neutral model can be downloaded from SMPLify.
    • All body models have to be renamed in SMPL_{GENDER}.pkl format.
      For example, mv basicModel_neutral_lbs_10_207_0_v1.0.0.pkl SMPL_NEUTRAL.pkl

  • SMPLX v1.1 is used in our experiments.

  • J_regressor_extra.npy

  • smpl_mean_params.npz

  • Download smpl_downsampling.npz from nkolot/GraphCMR.

  • Download mano_downsampling.npz from microsoft/MeshGraphormer.

  • Download the pre-trained model.

  • Download the partial_mesh files from PyMAF-X or use the following script: 

    mkdir mmhuman3d_download
cd mmhuman3d_download
wget -O mmhuman3d.7z -q https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmhuman3d/mmhuman3d.7z
7za x mmhuman3d.7z
cp -r mmhuman3d/data/partial_mesh/ ../data/
cd ..
rm -rf mmhuman3d_download

Download the above resources and arrange them in the following file structure:

mmhuman3d
├── mmhuman3d
├── docs
├── tests
├── tools
├── configs
└── data
    ├── body_models
    │   ├── J_regressor_extra.npy
    │   ├── smpl_mean_params.npz
    │   ├── smpl
    │   │   ├── SMPL_FEMALE.pkl
    │   │   ├── SMPL_MALE.pkl
    │   │   └── SMPL_NEUTRAL.pkl
    │   └── smplx
    │       ├── smplx_to_smpl.npz
    │       └── SMPLX_NEUTRAL.npz
    ├── partial_mesh
    │   └── *_vids.npz.npz
    ├── pretrained_models
    │   └── PyMAF-X_model_checkpoint.pth
    ├── mano_downsampling.npz
    └── smpl_downsampling.npz

Demo

By default, we use mmpose to detect 2d keypoints, and you can get the SMPL-X parameters as follow:

python demo/pymafx_estimate_smplx.py \
    --input_path demo/resources/multi_person_demo.mp4 \
    --output_path output \
    --visualization

If you want to reproduce the original repos, please install openpifpaf, then you will get the SMPL-X parameters as follow:

python demo/pymafx_estimate_smplx.py \
    --input_path demo/resources/multi_person_demo.mp4 \
    --output_path output \
    --visualization \
    --use_openpifpaf

You can find results in output.