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).

翻译：去噪扩散模型在生成多样且逼真的人体运动方面具有巨大潜力。然而，现有运动扩散模型在扩散过程中大多忽视物理定律，常生成物理上不合理的运动，并伴有明显伪影，如漂浮、脚部滑动和地面穿透。这严重影响了生成运动的质量，限制了其实际应用。为解决此问题，我们提出一种新颖的物理引导运动扩散模型（PhysDiff），将物理约束融入扩散过程。具体而言，我们提出一个基于物理的运动投影模块，利用物理模拟器中的运动模仿，将扩散步骤中的去噪运动投影为物理上合理的运动。投影后的运动进一步用于下一扩散步骤，以指导去噪扩散过程。直观上，我们模型中对物理的应用迭代地将运动拉向物理合理空间，这是简单后处理无法实现的。在大规模人体运动数据集上的实验表明，我们的方法实现了最先进的运动质量，并大幅提升了物理合理性（所有数据集提升超过78%）。