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Conference Paper

Tex2Shape: Detailed Full Human Body Geometry from a Single Image


Pons-Moll,  Gerard       
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;


Theobalt,  Christian       
Computer Graphics, MPI for Informatics, Max Planck Society;

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Alldieck, T., Pons-Moll, G., Theobalt, C., & Magnor, M. A. (2019). Tex2Shape: Detailed Full Human Body Geometry from a Single Image. In International Conference on Computer Vision (pp. 2293-2303). Piscataway, NJ: IEEE. doi:10.1109/ICCV.2019.00238.

Cite as: https://hdl.handle.net/21.11116/0000-0003-ECBE-E
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.