Skeleton Feature

Skeleton feature extraction and analysis is a rapidly developing field focusing on representing and utilizing the skeletal structure of objects, primarily humans, for various applications. Current research emphasizes improving the robustness and generalization of skeleton features, employing techniques like contrastive learning, transformer networks, and generative models to learn more discriminative and semantically rich representations from diverse data sources (e.g., 2D and 3D skeletal data, motion trajectories). These advancements are driving progress in areas such as person re-identification, action recognition, and virtual try-on, improving accuracy and addressing challenges like pose variation and data scarcity. The resulting improvements have significant implications for computer vision, human-computer interaction, and other fields requiring accurate and efficient analysis of human movement and shape.