  3D human pose estimation from a monocular image or 2D joints is an ill-posed
problem because of depth ambiguity and occluded joints. We argue that 3D human
pose estimation from a monocular input is an inverse problem where multiple
feasible solutions can exist. In this paper, we propose a novel approach to
generate multiple feasible hypotheses of the 3D pose from 2D joints.In contrast
to existing deep learning approaches which minimize a mean square error based
on an unimodal Gaussian distribution, our method is able to generate multiple
feasible hypotheses of 3D pose based on a multimodal mixture density networks.
Our experiments show that the 3D poses estimated by our approach from an input
of 2D joints are consistent in 2D reprojections, which supports our argument
that multiple solutions exist for the 2D-to-3D inverse problem. Furthermore, we
show state-of-the-art performance on the Human3.6M dataset in both best
hypothesis and multi-view settings, and we demonstrate the generalization
capacity of our model by testing on the MPII and MPI-INF-3DHP datasets. Our
code is available at the project website.
