Mesh2SMPL

Mesh2SMPL is a project that utilizes the MultiviewSMPLifyX and PaMIR projects to convert a textured mesh scan of a human into a SMPL model.

Installation

If it is your first time running Mesh2SMPL, please follow the instructions in docs/installation.md in order to setup everything necessary to run this program. This video also provides an accompanying guide for the installation instructions.

Usage

After you have completed all the necessary setup for Mesh2SMPL, follow the instructions in docs/run.md to run this program. This video also provides an accompanying guide for the run instructions.

Citation

If you use this code, please cite the following papers:

@ARTICLE{9321139,
  author={Zheng, Zerong and Yu, Tao and Liu, Yebin and Dai, Qionghai},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={PaMIR: Parametric Model-Conditioned Implicit Representation for Image-Based Human Reconstruction}, 
  year={2022},
  volume={44},
  number={6},
  pages={3170-3184},
  doi={10.1109/TPAMI.2021.3050505}}


@ARTICLE{SMPL:2015,
    author = {Loper, Matthew and Mahmood, Naureen and Romero, Javier and Pons-Moll, Gerard and Black, Michael J.},
    journal = {ACM Transactions on Graphics, (Proc. SIGGRAPH Asia)},
    title = {{SMPL}: A Skinned Multi-Person Linear Model}, 
    year = {2015},
    month = oct,
    volume = {34},
    number = {6},
    pages = {248:1--248:16},
    publisher = {ACM}}


@INPROCEEDINGS{8953319,
  author={Pavlakos, Georgios and Choutas, Vasileios and Ghorbani, Nima and Bolkart, Timo and Osman, Ahmed A. and Tzionas, Dimitrios and Black, Michael J.},
  booktitle={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={Expressive Body Capture: 3D Hands, Face, and Body From a Single Image}, 
  year={2019},
  volume={},
  number={},
  pages={10967-10977},
  doi={10.1109/CVPR.2019.01123}}


@ARTICLE{9954214,
  author={Fang, Hao-Shu and Li, Jiefeng and Tang, Hongyang and Xu, Chao and Zhu, Haoyi and Xiu, Yuliang and Li, Yong-Lu and Lu, Cewu},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time}, 
  year={2023},
  volume={45},
  number={6},
  pages={7157-7173},
  doi={10.1109/TPAMI.2022.3222784}}


@INPROCEEDINGS{8237518,
  author={Fang, Hao-Shu and Xie, Shuqin and Tai, Yu-Wing and Lu, Cewu},
  booktitle={2017 IEEE International Conference on Computer Vision (ICCV)}, 
  title={RMPE: Regional Multi-person Pose Estimation}, 
  year={2017},
  volume={},
  number={},
  pages={2353-2362},
  doi={10.1109/ICCV.2017.256}}


@INPROCEEDINGS{8954341,
  author={Li, Jiefeng and Wang, Can and Zhu, Hao and Mao, Yihuan and Fang, Hao-Shu and Lu, Cewu},
  booktitle={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={CrowdPose: Efficient Crowded Scenes Pose Estimation and a New Benchmark}, 
  year={2019},
  volume={},
  number={},
  pages={10855-10864},
  doi={10.1109/CVPR.2019.01112}}