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. 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).

提出一种新的物理引导的运动扩散模型 (PhysDiff)，该模型将物理约束条件融入到扩散过程中，并利用物理仿真中的运动模拟来将降噪后的运动投影为物理合理运动，以此指导降噪扩散过程，并在大规模人类运动数据集上进行实验，取得了最先进的运动质量和显着的物理合理性改进（对于所有数据集 > 78%）。