  Recent advances in 3D human shape estimation build upon parametric
representations that model very well the shape of the naked body, but are not
appropriate to represent the clothing geometry. In this paper, we present an
approach to model dressed humans and predict their geometry from single images.
We contribute in three fundamental aspects of the problem, namely, a new
dataset, a novel shape parameterization algorithm and an end-to-end deep
generative network for predicting shape.
  First, we present 3DPeople, a large-scale synthetic dataset with 2.5 Million
photo-realistic images of 80 subjects performing 70 activities and wearing
diverse outfits. Besides providing textured 3D meshes for clothes and body, we
annotate the dataset with segmentation masks, skeletons, depth, normal maps and
optical flow. All this together makes 3DPeople suitable for a plethora of
tasks.
  We then represent the 3D shapes using 2D geometry images. To build these
images we propose a novel spherical area-preserving parameterization algorithm
based on the optimal mass transportation method. We show this approach to
improve existing spherical maps which tend to shrink the elongated parts of the
full body models such as the arms and legs, making the geometry images
incomplete.
  Finally, we design a multi-resolution deep generative network that, given an
input image of a dressed human, predicts his/her geometry image (and thus the
clothed body shape) in an end-to-end manner. We obtain very promising results
in jointly capturing body pose and clothing shape, both for synthetic
validation and on the wild images.
