Tex2Shape: Detailed Full Human Body Geometry from a Single Image

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.
Author(s): | Thiemo Alldieck, Gerard Pons-Moll, Christian Theobalt, Marcus Magnor |
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Published: | October 2019 |
Type: | Article in conference proceedings |
Book: | IEEE International Conference on Computer Vision (ICCV) (IEEE) |
DOI: | 10.1109/ICCV.2019.00238 |
Presented at: | IEEE International Conference on Computer Vision (ICCV) 2019 |
Project(s): | Comprehensive Human Performance Capture from Monocular Video Footage Immersive Digital Reality |
BibTex PDF Link Suppl. Material Code arXiv
@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}}, doi = {10.1109/{ICCV}.2019.00238}, pages = {2293--2303}, month = {Oct}, year = {2019} }
Authors
Thiemo Alldieck
Fmr. ResearcherGerard Pons-Moll
ExternalChristian Theobalt
ExternalMarcus Magnor
Director, Chair