  Graph Convolutional Networks (GCNs) have been widely used in skeleton-based
human action recognition. In GCN-based methods, the spatio-temporal graph is
fundamental for capturing motion patterns. However, existing approaches ignore
the physical dependency and synchronized spatio-temporal correlations between
joints, which limits the representation capability of GCNs. To solve these
problems, we construct the directed diffusion graph for action modeling and
introduce the activity partition strategy to optimize the weight sharing
mechanism of graph convolution kernels. In addition, we present the
spatio-temporal synchronization encoder to embed synchronized spatio-temporal
semantics. Finally, we propose Directed Diffusion Graph Convolutional Network
(DD-GCN) for action recognition, and the experiments on three public datasets:
NTU-RGB+D, NTU-RGB+D 120, and NW-UCLA, demonstrate the state-of-the-art
performance of our method.
