  Dynamics of human body skeletons convey significant information for human
action recognition. Conventional approaches for modeling skeletons usually rely
on hand-crafted parts or traversal rules, thus resulting in limited expressive
power and difficulties of generalization. In this work, we propose a novel
model of dynamic skeletons called Spatial-Temporal Graph Convolutional Networks
(ST-GCN), which moves beyond the limitations of previous methods by
automatically learning both the spatial and temporal patterns from data. This
formulation not only leads to greater expressive power but also stronger
generalization capability. On two large datasets, Kinetics and NTU-RGBD, it
achieves substantial improvements over mainstream methods.
