Improving Robustness and Accuracy via Relative Information Encoding in 3D Human Pose Estimation

TL;DR

This paper introduces a relative information encoding approach for 3D human pose estimation that improves robustness to global motion and enhances local motion prediction by utilizing relative coordinates and temporal connections.

Contribution

It proposes a novel relative information encoding method combined with multi-stage optimization to improve 3D human pose estimation accuracy and robustness.

Findings

Outperforms state-of-the-art methods on two public datasets.

Enhances prediction of local motion with small movement ranges.

Increases robustness to global motion interference.

Abstract

Most of the existing 3D human pose estimation approaches mainly focus on predicting 3D positional relationships between the root joint and other human joints (local motion) instead of the overall trajectory of the human body (global motion). Despite the great progress achieved by these approaches, they are not robust to global motion, and lack the ability to accurately predict local motion with a small movement range. To alleviate these two problems, we propose a relative information encoding method that yields positional and temporal enhanced representations. Firstly, we encode positional information by utilizing relative coordinates of 2D poses to enhance the consistency between the input and output distribution. The same posture with different absolute 2D positions can be mapped to a common representation. It is beneficial to resist the interference of global motion on the prediction results. Second, we encode temporal information by establishing the connection between the current pose and other poses of the same person within a period of time. More attention will be paid to the movement changes before and after the current pose, resulting in better prediction performance on local motion with a small movement range. The ablation studies validate the effectiveness of the proposed relative information encoding method. Besides, we introduce a multi-stage optimization method to the whole framework to further exploit the positional and temporal enhanced representations. Our method outperforms state-of-the-art methods on two public datasets. Code is available at https://github.com/paTRICK-swk/Pose3D-RIE.