Skeletons in Flow: Graph Structured Flow Matching for Human Motion Prediction

Human motion prediction requires diverse future trajectories that remain consistent with observed motion and the articulated structure of the body. Skeletal constraints restrict individual poses, while coordinated motion also depends on spatial interactions (between connected joints) and temporal interactions (between time instants). We introduce Graph Structured Flow Matching (GSFM), which transports the complete future skeletal trajectory through one conditional velocity field. The trajectory forms a spatiotemporal skeleton graph, and spatial and temporal attention couple its evolution according to skeletal relations and physical time offsets. Bone directions lie on unit spheres relative to a root joint, and tangent evolution preserves prescribed bone lengths throughout generation. We train the field through conditional flow matching along geodesic paths connecting random trajectories centred on the last observed pose to recorded futures. Experiments on the archive of motion capture as surface shapes (AMASS) dataset evaluate prediction accuracy, diversity calibration, and motion statistics. We demonstrate the contributions of spatial and temporal message passing in the proposed architecture through an ablation study. GSFM models trained on AMASS also perform competitively on the Human3.6M skeleton without parameter updates or retraining, demonstrating applicability to an unseen skeletal structure.