  Denoising diffusion models hold great promise for generating diverse and
realistic human motions. However, existing motion diffusion models largely
disregard the laws of physics in the diffusion process and often generate
physically-implausible motions with pronounced artifacts such as floating, foot
sliding, and ground penetration. This seriously impacts the quality of
generated motions and limits their real-world application. To address this
issue, we present a novel physics-guided motion diffusion model (PhysDiff),
which incorporates physical constraints into the diffusion process.
Specifically, we propose a physics-based motion projection module that uses
motion imitation in a physics simulator to project the denoised motion of a
diffusion step to a physically-plausible motion. The projected motion is
further used in the next diffusion step to guide the denoising diffusion
process. Intuitively, the use of physics in our model iteratively pulls the
motion toward a physically-plausible space, which cannot be achieved by simple
post-processing. Experiments on large-scale human motion datasets show that our
approach achieves state-of-the-art motion quality and improves physical
plausibility drastically (>78% for all datasets).
