Body Tracking

Body tracking aims to accurately capture and represent human movement, enabling applications ranging from virtual reality and robotics to healthcare and entertainment. Current research focuses on developing robust and versatile methods using diverse input modalities, including LiDAR, IMUs, and cameras, often employing deep learning architectures like diffusion models, transformers, and variational autoencoders to process and synthesize motion data. These advancements are driving improvements in the realism and accuracy of virtual avatars, enabling more intuitive human-computer interaction, and facilitating innovative applications in areas such as rehabilitation and surgical assistance.