  Monocular 3D human-pose estimation from static images is a challenging
problem, due to the curse of dimensionality and the ill-posed nature of lifting
2D-to-3D. In this paper, we propose a Deep Conditional Variational Autoencoder
based model that synthesizes diverse anatomically plausible 3D-pose samples
conditioned on the estimated 2D-pose. We show that CVAE-based 3D-pose sample
set is consistent with the 2D-pose and helps tackling the inherent ambiguity in
2D-to-3D lifting. We propose two strategies for obtaining the final 3D pose-
(a) depth-ordering/ordinal relations to score and weight-average the candidate
3D-poses, referred to as OrdinalScore, and (b) with supervision from an Oracle.
We report close to state of-the-art results on two benchmark datasets using
OrdinalScore, and state-of-the-art results using the Oracle. We also show that
our pipeline yields competitive results without paired image-to-3D annotations.
The training and evaluation code is available at
https://github.com/ssfootball04/generative_pose.
