Multi Modal Skeleton Input

Multi-modal skeleton input leverages diverse data sources, such as 2D/3D poses and motion capture data, to improve the accuracy and robustness of human pose estimation, action recognition, and character animation. Current research focuses on developing unified frameworks that efficiently fuse information from multiple modalities, often employing early fusion strategies and techniques like knowledge distillation to address challenges like dataset inconsistencies and modality bias. These advancements are significant for applications ranging from human-computer interaction to realistic character animation, enabling more accurate and natural representations of human movement and behavior.