Due　to　self-occlusion　and　high　degree　of　freedom,　estimating　3D　hand　pose　from　a　single　RGB　image　is　a　great　challenging　problem.　Graph　convolutional　networks　(GCNs)　use　graphs　to　describe　the　physical　connection　relationships　between　hand　joints　and　improve　the　accuracy　of　3D　hand　pose　regression.　However,　GCNs　cannot　effectively　describe　the　relationships　between　non-adjacent　hand　joints.　Recently,　hypergraph　convolutional　networks　(HGCNs)　have　received　much　attention　as　they　can　describe　multi-dimensional　relationships　between　nodes　through　hyperedges;　therefore,　this　paper　proposes　a　framework　for　3D　hand　pose　estimation　based　on　HGCN,　which　can　better　extract　correlated　relationships　between　adjacent　and　non-adjacent　hand　joints.　To　overcome　the　shortcomings　of　predefined　hypergraph　structures,　a　kind　of　dynamic　hypergraph　convolutional　network　is　proposed,　in　which　hyperedges　are　constructed　dynamically　based　on　hand　joint　feature　similarity.　To　better　explore　the　local　semantic　relationships　between　nodes,　a　kind　of　semantic　dynamic　hypergraph　convolution　is　proposed.　The　proposed　method　is　evaluated　on　publicly　available　benchmark　datasets.　Qualitative　and　quantitative　experimental　results　both　show　that　the　proposed　HGCN　and　improved　methods　for　3D　hand　pose　estimation　are　better　than　GCN,　and　achieve　state-of-the-art　performance　compared　with　existing　methods.　©　2024,　Shanghai　Jiao　Tong　University.