  3D human pose estimation from monocular images is a highly ill-posed problem
due to depth ambiguities and occlusions. Nonetheless, most existing works
ignore these ambiguities and only estimate a single solution. In contrast, we
generate a diverse set of hypotheses that represents the full posterior
distribution of feasible 3D poses. To this end, we propose a normalizing flow
based method that exploits the deterministic 3D-to-2D mapping to solve the
ambiguous inverse 2D-to-3D problem. Additionally, uncertain detections and
occlusions are effectively modeled by incorporating uncertainty information of
the 2D detector as condition. Further keys to success are a learned 3D pose
prior and a generalization of the best-of-M loss. We evaluate our approach on
the two benchmark datasets Human3.6M and MPI-INF-3DHP, outperforming all
comparable methods in most metrics. The implementation is available on GitHub.
