  This work addresses the problem of estimating the full body 3D human pose and
shape from a single color image. This is a task where iterative
optimization-based solutions have typically prevailed, while Convolutional
Networks (ConvNets) have suffered because of the lack of training data and
their low resolution 3D predictions. Our work aims to bridge this gap and
proposes an efficient and effective direct prediction method based on ConvNets.
Central part to our approach is the incorporation of a parametric statistical
body shape model (SMPL) within our end-to-end framework. This allows us to get
very detailed 3D mesh results, while requiring estimation only of a small
number of parameters, making it friendly for direct network prediction.
Interestingly, we demonstrate that these parameters can be predicted reliably
only from 2D keypoints and masks. These are typical outputs of generic 2D human
analysis ConvNets, allowing us to relax the massive requirement that images
with 3D shape ground truth are available for training. Simultaneously, by
maintaining differentiability, at training time we generate the 3D mesh from
the estimated parameters and optimize explicitly for the surface using a 3D
per-vertex loss. Finally, a differentiable renderer is employed to project the
3D mesh to the image, which enables further refinement of the network, by
optimizing for the consistency of the projection with 2D annotations (i.e., 2D
keypoints or masks). The proposed approach outperforms previous baselines on
this task and offers an attractive solution for direct prediction of 3D shape
from a single color image.
