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Various deep learning techniques have been proposed to solve the single-view2D-to-3D pose estimation problem. While the average prediction accuracy hasbeen improved significantly over the years, the performance on hard poses withdepth ambiguity, self-occlusion, and complex or rare poses is still far fromsatisfactory. In this work, we target these hard poses and present a novelskeletal GNN learning solution. To be specific, we propose a hop-awarehierarchical channel-squeezing fusion layer to effectively extract relevantinformation from neighboring nodes while suppressing undesired noises in GNNlearning. In addition, we propose a temporal-aware dynamic graph constructionprocedure that is robust and effective for 3D pose estimation. Experimentalresults on the Human3.6M dataset show that our solution achieves 10.3\% averageprediction accuracy improvement and greatly improves on hard poses overstate-of-the-art techniques. We further apply the proposed technique on theskeleton-based action recognition task and also achieve state-of-the-artperformance. Our code is available athttps://github.com/ailingzengzzz/Skeletal-GNN.

Learning Skeletal Graph Neural Networks for Hard 3D Pose Estimation