3D-UGCN: A Unified Graph Convolutional Network for Robust 3D Human Pose Estimation from Monocular RGB Images

TL;DR

This paper introduces 3D-UGCN, a unified graph convolutional network designed to enhance 3D human pose estimation from monocular RGB images, effectively handling occlusions and missing data in single-view videos.

Contribution

The paper presents an improved UGCN model that processes 3D pose data and addresses occlusion issues in 3D human pose estimation from monocular videos.

Findings

Improved accuracy in 3D pose estimation.

Effective handling of occlusions and missing skeleton data.

Enhanced robustness in single-view video analysis.

Abstract

Human pose estimation remains a multifaceted challenge in computer vision, pivotal across diverse domains such as behavior recognition, human-computer interaction, and pedestrian tracking. This paper proposes an improved method based on the spatial-temporal graph convolution net-work (UGCN) to address the issue of missing human posture skeleton sequences in single-view videos. We present the improved UGCN, which allows the network to process 3D human pose data and improves the 3D human pose skeleton sequence, thereby resolving the occlusion issue.