Fig. 5
From: Hierarchical intertwined graph representation learning for skeleton-based action recognition
Action recognition results of HI-GCN on skeleton-based sequences. The model demonstrates robust performance across diverse action categories including: (a) object manipulation tasks (juggle table tennis ball, play magic cube), (b) sports motions (tennis bat swing, shoot at basket), (c) tool usage (shoot with gun, play guitar), and (d) human–computer interactions (use laptop, type on keyboard). The skeletal overlays highlight HI-GCN’s ability to capture both fine-grained hand-object interactions and full-body coordination patterns.
