Tex2Shape: Detailed Full Human Body Geometry from a Single Image

Tex2Shape Network and Weights

Thiemo Alldieck1, Gerard Pons-Moll2, Christian Theobalt2 and Marcus Magnor1

1Computer Graphics Lab, TU Braunschweig 
2Max Planck Institute for Informatics, Saarland Informatics Campus

ICCV 2019 Seoul, Korea

Abstract

We present a simple yet effective method to infer detailed full human body shape from only a single photograph. Our model can infer full-body shape including face, hair, and clothing including wrinkles at interactive frame-rates. Results feature details even on parts that are occluded in the input image. Our main idea is to turn shape regression into an aligned image-to-image translation problem. The input to our method is a partial texture map of the visible region obtained from off-the-shelf methods. From a partial texture, we estimate detailed normal and vector displacement maps, which can be applied to a low-resolution smooth body model to add detail and clothing. Despite being trained purely with synthetic data, our model generalizes well to real-world photographs. Numerous results demonstrate the versatility and robustness of our method.

Tex2Shape Model

License

Copyright (c) 2019 Thiemo Alldieck, Technische Universität Braunschweig, Max-Planck-Gesellschaft

Please read carefully the following terms and conditions and any accompanying documentation before you download and/or use this software and associated documentation files (the "Software").

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Clone from GitHub

Citation

@inproceedings{alldieck2019tex2shape,
  title = {Tex2Shape: Detailed Full Human Body Geometry from a Single Image},
  author = {Alldieck, Thiemo and Pons-Moll, Gerard and Theobalt, Christian and Magnor, Marcus},
  booktitle = {{IEEE} International Conference on Computer Vision ({ICCV})},
  organization = {{IEEE}},
  year = {2019}
}