H 2 GCN: A hybrid hypergraph convolution network for skeleton-based action recognition

Recent GCN-based works have achieved remarkable results for skeleton -based human action recognition. Nevertheless, while existing approaches extensively investigate pairwise joint relationships, only a limited number of models explore the intricate, high -order relationships among multiple joints. In this paper, we propose a novel hypergraph convolution method that represents the relationships among multiple joints with hyperedges, and dynamically refines the height -order relationship between hyperedges in the spatial, temporal, and channel dimensions. Specifically, our method initiates with a temporal -channel refinement hypergraph convolutional network, dynamically learning temporal and channel topologies in a data -dependent manner, which facilitates the capture of non-physical structural information inherent in the human body. Furthermore, to model various inter -joint relationships across spatio-temporal dimensions, we propose a spatio-temporal hypergraph joint module, which aims to encapsulate the dynamic spatial-temporal characteristics of the human body. Through the integration of these modules, our proposed model achieves state-of-the-art performance on RGB+D 60 and NTU RGB+D 120 datasets.