Skeleton Based Human Motion

Skeleton-based human motion analysis focuses on understanding and modeling human movement using skeletal representations, aiming to predict future poses, synthesize realistic motions, and ensure privacy in motion data. Current research heavily utilizes graph convolutional networks (GCNs) and other deep learning architectures like MLP-Mixers and diffusion probabilistic models to achieve these goals, with a growing emphasis on improving prediction accuracy, especially over longer time horizons, and developing more robust evaluation metrics. This field has significant implications for various applications, including virtual reality, robotics (human-robot interaction), and animation, as well as for addressing privacy concerns related to the use of motion capture data.