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In this paper we predict a full 3D avatar of a person from a single image. Weinfer texture and geometry in the UV-space of the SMPL model using animage-to-image translation method. Given partial texture and segmentationlayout maps derived from the input view, our model predicts the completesegmentation map, the complete texture map, and a displacement map. Thepredicted maps can be applied to the SMPL model in order to naturallygeneralize to novel poses, shapes, and even new clothing. In order to learn ourmodel in a common UV-space, we non-rigidly register the SMPL model to thousandsof 3D scans, effectively encoding textures and geometries as images incorrespondence. This turns a difficult 3D inference task into a simplerimage-to-image translation one. Results on rendered scans of people and imagesfrom the DeepFashion dataset demonstrate that our method can reconstructplausible 3D avatars from a single image. We further use our model to digitallychange pose, shape, swap garments between people and edit clothing. Toencourage research in this direction we will make the source code available forresearch purpose.

360-Degree Textures of People in Clothing from a Single Image