Skeleton Sequence

Skeleton sequence analysis focuses on understanding and modeling the temporal dynamics of human or animal movement captured as sequences of skeletal joint positions. Current research emphasizes self-supervised learning methods, often employing transformer networks and graph convolutional networks, to learn robust representations from unlabeled data and address challenges like occlusion and noisy data. These advancements are improving accuracy in applications such as action recognition, gait analysis, and human-computer interaction, while also enabling novel approaches to anomaly detection and motion synthesis. The field's impact extends to diverse areas including healthcare, robotics, and animation, where accurate and efficient analysis of movement is crucial.