Tex2Shape: Detailed Full Human Body Geometry from a Single Image
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.
Cite
Text
Alldieck et al. "Tex2Shape: Detailed Full Human Body Geometry from a Single Image." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019. doi:10.1109/ICCV.2019.00238Markdown
[Alldieck et al. "Tex2Shape: Detailed Full Human Body Geometry from a Single Image." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.](https://mlanthology.org/iccv/2019/alldieck2019iccv-tex2shape/) doi:10.1109/ICCV.2019.00238BibTeX
@inproceedings{alldieck2019iccv-tex2shape,
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 = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
year = {2019},
doi = {10.1109/ICCV.2019.00238},
url = {https://mlanthology.org/iccv/2019/alldieck2019iccv-tex2shape/}
}