  3D human pose estimation from a single image is an inverse problem due to the
inherent ambiguity of the missing depth. Several previous works addressed the
inverse problem by generating multiple hypotheses. However, these works are
strongly supervised and require ground truth 2D-to-3D correspondences which can
be difficult to obtain. In this paper, we propose a weakly supervised deep
generative network to address the inverse problem and circumvent the need for
ground truth 2D-to-3D correspondences. To this end, we design our network to
model a proposal distribution which we use to approximate the unknown
multi-modal target posterior distribution. We achieve the approximation by
minimizing the KL divergence between the proposal and target distributions, and
this leads to a 2D reprojection error and a prior loss term that can be weakly
supervised. Furthermore, we determine the most probable solution as the
conditional mode of the samples using the mean-shift algorithm. We evaluate our
method on three benchmark datasets -- Human3.6M, MPII and MPI-INF-3DHP.
Experimental results show that our approach is capable of generating multiple
feasible hypotheses and achieves state-of-the-art results compared to existing
weakly supervised approaches. Our source code is available at the project
website.
