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Learning to Dress 3D People in Generative Clothing

Qianli Ma, Jinlong Yang, Anurag Ranjan, Sergi Pujades, Gerard Pons-Moll, Siyu Tang, and Michael. J. Black
Computer Vision and Pattern Recognition (CVPR) 2020, Seattle, WA

CAPE is a Graph-CNN based generative model for dressing 3D meshes of human body. It is compatible with the popular body model, SMPL, and can generalize to diverse body shapes and body poses. It is designed to be "plug-and-play" for many applications that already use SMPL.

The CAPE Dataset provides SMPL mesh registration of 4D scans of people in clothing, along with registered scans of the ground truth body shapes under clothing. 

Abstract

Three-dimensional human body models are widely used in the analysis of human pose and motion. Existing models, however, are learned from minimally-clothed 3D scans and thus do not generalize to the complexity of dressed people in common images and videos. Additionally, current models lack the expressive power needed to represent the complex non-linear geometry of pose-dependent clothing shape. To address this, we learn a generative 3D mesh model of clothed people from 3D scans with varying pose and clothing. Specifically, we train a conditional Mesh-VAE-GAN to learn the clothing deformation from the SMPL body model, making clothing an additional term on SMPL. Our model is conditioned on both pose and clothing type, giving the ability to draw samples of clothing to dress different body shapes in a variety of styles and poses. To preserve wrinkle detail, our Mesh-VAE-GAN extends patchwise discriminators to 3D meshes. Our model, named CAPE, represents global shape and fine local structure, effectively extending the SMPL body model to clothing. To our knowledge, this is the first generative model that directly dresses 3D human body meshes and generalizes to different poses. The model, code and data are available for research purposes at this website.


More Information

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  • 1-min spotlight video
  • 4-min detailed video

Citing the CAPE Model and Dataset


If you find the CAPE model and dataset useful to your research, please cite our work:

@inproceedings{CAPE:CVPR:20,
  title = {{Learning to Dress 3D People in Generative Clothing}},
  author = {Ma, Qianli and Yang, Jinlong and Ranjan, Anurag and Pujades, Sergi and Pons-Moll, Gerard and Tang, Siyu and Black, Michael J.},
  booktitle = {Computer Vision and Pattern Recognition (CVPR)},
  month = June,
  year = {2020},
  month_numeric = {6}}

 

If you use the CAPE dataset, please also reference ClothCap:

@article{Pons-Moll:Siggraph2017,
  title = {ClothCap: Seamless 4D Clothing Capture and Retargeting},
  author = {Pons-Moll, Gerard and Pujades, Sergi and Hu, Sonny and Black, Michael},
  journal = {ACM Transactions on Graphics, (Proc. SIGGRAPH)},
  volume = {36},
  number = {4},
  year = {2017},
  note = {Two first authors contributed equally},
  crossref = {},
  url = {http://dx.doi.org/10.1145/3072959.3073711}
}

Contact

For questions, please contact cape@tue.mpg.de.

For commercial licensing, please contact ps-licensing@tue.mpg.de.

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