Geometry-Constrained Dynamic Hypergraph Convolutional Network with Contrastive Score Refinement for Skeleton-Based Action Recognition

Minh Ta Tran Nhat, Yuning Ye, Yuseok Ban

Skeleton-based action recognition is a pivotal task in computer vision; however, conventional Graph Convolutional Networks (GCNs) are often constrained by pairwise joint modeling, which fails to capture the intricate high-order interactions inherent in human movement. While recent hypergraph-based methods attempt to address this limitation, they typically rely on static structures derived from anatomical priors, lacking the flexibility to capture dynamic, action-specific joint correlations. To this end, we propose DyGCN, a Geometry-Constrained Dynamic Hypergraph Convolutional Network for skeleton-based action recognition. At the core of DyGCN is the Dynamic Hypergraph Construction Network with Contrastive Score Refinement (DyGCT) block, which comprises two novel components: the Geometry-Constrained Dynamic Hypergraph Construction (GeoHC) module and the Contrastive Score Refinement (CSR) module. Specifically, GeoHC dynamically constructs action-specific hyperedges by leveraging geometry-constrained second-order compatibility, transcending the limitations of static hyperedge structures. Furthermore, CSR explicitly refines the pairwise compatibility scores during hypergraph…