  3D pose estimation has recently gained substantial interests in computer
vision domain. Existing 3D pose estimation methods have a strong reliance on
large size well-annotated 3D pose datasets, and they suffer poor model
generalization on unseen poses due to limited diversity of 3D poses in training
sets. In this work, we propose PoseGU, a novel human pose generator that
generates diverse poses with access only to a small size of seed samples, while
equipping the Counterfactual Risk Minimization to pursue an unbiased evaluation
objective. Extensive experiments demonstrate PoseGU outforms almost all the
state-of-the-art 3D human pose methods under consideration over three popular
benchmark datasets. Empirical analysis also proves PoseGU generates 3D poses
with improved data diversity and better generalization ability.
