  In this paper we predict a full 3D avatar of a person from a single image. We
infer texture and geometry in the UV-space of the SMPL model using an
image-to-image translation method. Given partial texture and segmentation
layout maps derived from the input view, our model predicts the complete
segmentation map, the complete texture map, and a displacement map. The
predicted maps can be applied to the SMPL model in order to naturally
generalize to novel poses, shapes, and even new clothing. In order to learn our
model in a common UV-space, we non-rigidly register the SMPL model to thousands
of 3D scans, effectively encoding textures and geometries as images in
correspondence. This turns a difficult 3D inference task into a simpler
image-to-image translation one. Results on rendered scans of people and images
from the DeepFashion dataset demonstrate that our method can reconstruct
plausible 3D avatars from a single image. We further use our model to digitally
change pose, shape, swap garments between people and edit clothing. To
encourage research in this direction we will make the source code available for
research purpose.
