Learning Skeletal Graph Neural Networks for Hard 3D Pose Estimation

Understand

Various deep learning techniques have been proposed to solve the single-view 2D-to-3D pose estimation problem.

• While the average prediction accuracy has been improved significantly over the years, the performance on hard poses with depth ambiguity, self-occlusion, and complex or rare poses is still far from satisfactory.
• In this work, we target these hard poses and present a novel skeletal GNN learning solution.
• To be specific, we propose a hop-aware hierarchical channel-squeezing fusion layer to effectively extract relevant information from neighboring nodes while suppressing undesired noises in GNN learning.