Generating realistic motions for digital humans is time-consuming for many graphics applications. Data-driven motion synthesis approaches have seen solid progress in recent years through deep generative models. These results offer high-quality motions but typically suffer in motion style diversity. For the first time, we propose a framework using the denoising diffusion probabilistic model (DDPM) to synthesize styled human motions, integrating two tasks into one pipeline with increased style diversity compared with traditional motion synthesis methods. Experimental results show that our system can generate high-quality and diverse walking motions.
TY - CONF
Edmund J. C. Findlay, Haozheng Zhang, Ziyi Chang and Hubert P. H. Shum, "Denoising Diffusion Probabilistic Models for Styled Walking Synthesis," in MIG '22: Proceedings of the 2022 ACM SIGGRAPH Conference on Motion, Interaction and Games, Guanajuato, Mexico, ACM, 2022.
Last updated on 17 September 2023