The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation

TL;DR

This paper identifies biases in data processing methods like flipping and keypoint transformations that hinder human pose estimation accuracy, and proposes an unbiased data processing approach to improve performance and reproducibility.

Contribution

The paper introduces Unbiased Data Processing (UDP), a model-agnostic method addressing biases in coordinate and keypoint transformations in human pose estimation.

Findings

UDP improves pose estimation accuracy across multiple benchmarks.

Unbiased transformations lead to more reproducible and fair comparisons.

The approach sets a new reliable baseline for future research.

Abstract

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