  We propose a new 2D pose refinement network that learns to predict the human
bias in the estimated 2D pose. There are biases in 2D pose estimations that are
due to differences between annotations of 2D joint locations based on
annotators' perception and those defined by motion capture (MoCap) systems.
These biases are crafted into publicly available 2D pose datasets and cannot be
removed with existing error reduction approaches. Our proposed pose refinement
network allows us to efficiently remove the human bias in the estimated 2D
poses and achieve highly accurate multi-view 3D human pose estimation.
