  Skeleton-based action recognition relies on the extraction of
spatial-temporal topological information. Hypergraphs can establish prior
unnatural dependencies for the skeleton. However, the existing methods only
focus on the construction of spatial topology and ignore the time-point
dependence. This paper proposes a dynamic spatial-temporal hypergraph
convolutional network (DST-HCN) to capture spatial-temporal information for
skeleton-based action recognition. DST-HCN introduces a time-point hypergraph
(TPH) to learn relationships at time points. With multiple spatial static
hypergraphs and dynamic TPH, our network can learn more complete
spatial-temporal features. In addition, we use the high-order information
fusion module (HIF) to fuse spatial-temporal information synchronously.
Extensive experiments on NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets show
that our model achieves state-of-the-art, especially compared with hypergraph
methods.
