articleOct 1, 2023Closed access

PhysDiff: Physics-Guided Human Motion Diffusion Model

Indexed incrossref

Abstract

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…

Citation impact

189
total citations
FWCI
21.53
Percentile
100%
References
127
Citations per year

Authors

5

Topics & keywords

Keywords
  • Diffusion process
  • Motion (physics)
  • Diffusion
  • Computer science
  • Noise reduction
  • Artificial intelligence
  • Computer vision
  • Statistical physics
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