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.
@article{arxiv.1904.08645,
title = {Tex2Shape: Detailed Full Human Body Geometry From a Single Image},
author = {Thiemo Alldieck and Gerard Pons-Moll and Christian Theobalt and Marcus Magnor},
journal= {arXiv preprint arXiv:1904.08645},
year = {2019}
}