  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. Experimental
results on the Human3.6M dataset show that our solution achieves 10.3\% average
prediction accuracy improvement and greatly improves on hard poses over
state-of-the-art techniques. We further apply the proposed technique on the
skeleton-based action recognition task and also achieve state-of-the-art
performance. Our code is available at
https://github.com/ailingzengzzz/Skeletal-GNN.
