Human Motion Diffusion Model

Human motion diffusion models aim to generate realistic and diverse human movements, often conditioned on text descriptions, audio, or other modalities. Current research heavily utilizes transformer-based diffusion models, focusing on improvements in physical plausibility (e.g., preventing unrealistic movements like floating), handling complex or long motions, and enabling fine-grained control over generated sequences through techniques like anatomical decomposition and cross-diffusion. These advancements have significant implications for animation, virtual reality, and robotics, offering more natural and expressive human-like movement in various applications.