  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.
