Skeletal-GNN

Code for "Learning Skeletal Graph Neural Networks for Hard 3D Pose Estimation" ICCV'21

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. In addition, we propose a temporal-aware dynamic graph construction procedure that is robust and effective for 3D pose estimation. Last, we further apply the proposed technique on the skeleton-based action recognition task and also achieve state-of-the-art performance.

Code will be coming soon!