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Recent advances in 3D human shape estimation build upon parametricrepresentations that model very well the shape of the naked body, but are notappropriate to represent the clothing geometry. In this paper, we present anapproach to model dressed humans and predict their geometry from single images.We contribute in three fundamental aspects of the problem, namely, a newdataset, a novel shape parameterization algorithm and an end-to-end deepgenerative network for predicting shape.  First, we present 3DPeople, a large-scale synthetic dataset with 2.5 Millionphoto-realistic images of 80 subjects performing 70 activities and wearingdiverse outfits. Besides providing textured 3D meshes for clothes and body, weannotate the dataset with segmentation masks, skeletons, depth, normal maps andoptical flow. All this together makes 3DPeople suitable for a plethora oftasks.  We then represent the 3D shapes using 2D geometry images. To build theseimages we propose a novel spherical area-preserving parameterization algorithmbased on the optimal mass transportation method. We show this approach toimprove existing spherical maps which tend to shrink the elongated parts of thefull body models such as the arms and legs, making the geometry imagesincomplete.  Finally, we design a multi-resolution deep generative network that, given aninput image of a dressed human, predicts his/her geometry image (and thus theclothed body shape) in an end-to-end manner. We obtain very promising resultsin jointly capturing body pose and clothing shape, both for syntheticvalidation and on the wild images.

3DPeople: Modeling the Geometry of Dressed Humans