Taming Diffusion Probabilistic Models for Character Control

arXiv:2404.15121v1 Announce Type: cross
Abstract: We present a novel character control framework that effectively utilizes motion diffusion probabilistic models to generate high-quality and diverse character animations, responding in real-time to a variety of dynamic user-supplied control signals. At the heart of our method lies a transformer-based Conditional Autoregressive Motion Diffusion Model (CAMDM), which takes as input the character's historical motion and can generate a range of diverse potential future motions conditioned on high-level, coarse user control. To meet the demands …

arxiv control cs.ai cs.cv cs.gr diffusion type