  In this paper, we address the problem of learning 3D human pose and body
shape from 2D image dataset, without having to use 3D dataset (body shape and
pose). The idea is to use dense correspondences between image points and a body
surface, which can be annotated on in-the wild 2D images, and extract and
aggregate 3D information from them. To do so, we propose a training strategy
called ``deform-and-learn" where we alternate deformable surface registration
and training of deep convolutional neural networks (ConvNets). Unlike previous
approaches, our method does not require 3D pose annotations from a motion
capture (MoCap) system or human intervention to validate 3D pose annotations.
