  Being a fundamental component in training and inference, data processing has
not been systematically considered in human pose estimation community, to the
best of our knowledge. In this paper, we focus on this problem and find that
the devil of human pose estimation evolution is in the biased data processing.
Specifically, by investigating the standard data processing in state-of-the-art
approaches mainly including coordinate system transformation and keypoint
format transformation (i.e., encoding and decoding), we find that the results
obtained by common flipping strategy are unaligned with the original ones in
inference. Moreover, there is a statistical error in some keypoint format
transformation methods. Two problems couple together, significantly degrade the
pose estimation performance and thus lay a trap for the research community.
This trap has given bone to many suboptimal remedies, which are always
unreported, confusing but influential. By causing failure in reproduction and
unfair in comparison, the unreported remedies seriously impedes the
technological development. To tackle this dilemma from the source, we propose
Unbiased Data Processing (UDP) consist of two technique aspect for the two
aforementioned problems respectively (i.e., unbiased coordinate system
transformation and unbiased keypoint format transformation). As a
model-agnostic approach and a superior solution, UDP successfully pushes the
performance boundary of human pose estimation and offers a higher and more
reliable baseline for research community. Code is public available in
https://github.com/HuangJunJie2017/UDP-Pose
