  Recovering 3D full-body human pose is a challenging problem with many
applications. It has been successfully addressed by motion capture systems with
body worn markers and multiple cameras. In this paper, we address the more
challenging case of not only using a single camera but also not leveraging
markers: going directly from 2D appearance to 3D geometry. Deep learning
approaches have shown remarkable abilities to discriminatively learn 2D
appearance features. The missing piece is how to integrate 2D, 3D and temporal
information to recover 3D geometry and account for the uncertainties arising
from the discriminative model. We introduce a novel approach that treats 2D
joint locations as latent variables whose uncertainty distributions are given
by a deep fully convolutional neural network. The unknown 3D poses are modeled
by a sparse representation and the 3D parameter estimates are realized via an
Expectation-Maximization algorithm, where it is shown that the 2D joint
location uncertainties can be conveniently marginalized out during inference.
Extensive evaluation on benchmark datasets shows that the proposed approach
achieves greater accuracy over state-of-the-art baselines. Notably, the
proposed approach does not require synchronized 2D-3D data for training and is
applicable to "in-the-wild" images, which is demonstrated with the MPII
dataset.
