Motion Semantics

Motion semantics research focuses on understanding and manipulating the meaning embedded within human and other character movements. Current efforts concentrate on developing models, often leveraging generative adversarial networks (GANs), variational autoencoders (VAEs), and large language models (LLMs), to accurately predict, generate, and retarget motion while preserving semantic information. This involves techniques like learning latent representations of motion semantics and incorporating vision-language models to bridge the gap between visual motion data and textual descriptions. The field's advancements have significant implications for animation, robotics, and human-computer interaction, enabling more realistic and controllable character animation and improved motion capture technologies.