Skeleton Graph

Skeleton graphs represent human pose and movement data as interconnected nodes (joints) and edges (relationships), enabling efficient analysis of complex spatiotemporal patterns. Current research focuses on developing sophisticated graph convolutional networks (GCNs) and transformer-based models to analyze these graphs for applications like action recognition, sign language interpretation, and person re-identification, often incorporating techniques like contrastive learning and adaptive graph structures. These advancements improve the accuracy and efficiency of various tasks by leveraging the rich relational information inherent in skeletal data, impacting fields ranging from human-computer interaction to robotics.