Tex2Shape: Detailed Full Human Body Geometry From a Single Image
Thiemo Alldieck, Gerard Pons-Moll, Christian Theobalt, Marcus A. Magnor
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.
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Cited by top-tier papers108
- Animatable Neural Radiance Fields for Modeling Dynamic Human BodiesSida Peng, Junting Dong, Qianqian Wang, Shangzhan Zhang et al.ICCV 2021 · 461 citations
- Multi-Garment Net: Learning to Dress 3D People From ImagesBharat Lal Bhatnagar, Garvita Tiwari, Christian Theobalt, Gerard Pons-MollICCV 2019 · 447 citations
- Neural Unsigned Distance Fields for Implicit Function LearningJulian Chibane, Aymen Mir, Gerard Pons-MollNeurIPS 2020 · 415 citations
- A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and PoseShih-Yang Su, Frank Yu, Michael Zollhöfer, Helge RhodinNeurIPS 2021 · 316 citations
- ICON: Implicit Clothed humans Obtained from NormalsYuliang Xiu, Jinlong Yang, Dimitrios Tzionas, Michael J. BlackCVPR 2022 · 286 citations
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