Graph network learning for human skeleton modeling: a survey

Over the past few decades, Human Skeleton Modeling (HSM) has gained considerable attention in computer vision, exploring various practical applications such as the video surveillance, the human-computer interaction, the medical assistance analysis, and the autonomous driving through images and videos. The performance of HSM and its applications on challenging datasets has been significantly improved due to recent advancements of deep learning methods. These advancements have been extended to non-Euclidean or graph data with multiple nodes and edges. Because human joints and skeleton combinations are represented as graph structures, graph networks are appropriate for the non-Euclidean HSM. In recent years, graph networks have become essential tools for the HSM and behavioral analyses. However, prior surveys are often siloed, focusing either on a narrow class of models such as GCNs or on a single application like action recognition. A unified framework that systematically analyzes diverse graph network learning paradigms across the entire HSM pipeline has been notably absent. We conduct a survey of graph network methods for HSM and their application domains. This comprehensive overview includes a taxonomy of graph network techniques, a detailed study of benchmark datasets for HSM, extensive descriptions of the performance of graph networks in three major application domains, and a collection of related resources and open-source codes. Finally, we provided insightful recommendations for future research directions and trends of graph networks for HSM. This survey serves as the introductory material for beginners in graph network-based HSM and as the reference materials for advanced researchers.