Personalized Motion

Personalized motion research focuses on creating models that accurately predict and generate human movement tailored to individual characteristics, addressing limitations of generic motion models. Current efforts utilize techniques like instance-based transfer learning, counterfactual algorithms, and diffusion models to achieve this personalization, often incorporating wearable sensor data or skeleton-based representations for improved accuracy and control. This field is significant for applications ranging from personalized rehabilitation robotics and sports training to enhancing human-robot interaction and creating more realistic and expressive digital characters in animation and virtual environments.