Title: Physics-Guided Motion Loss for Video Generation Model
Primary area: generative models
Keywords: Video generation, Diffusion model

Abstract: Current video diffusion models generate visually compelling content but often violate 
basic laws of physics, producing subtle artifacts like rubber-sheet deformations and 
inconsistent object motion. We introduce a frequency-domain physics prior that improves 
motion plausibility without modifying model architectures. Our method decomposes common 
rigid motions (translation, rotation, scaling) into lightweight spectral losses, 
requiring only 2.7% of frequency coefficients while preserving 97%+ of spectral energy. 
Applied to Open-Sora, MVDIT, and Hunyuan, our approach improves both motion accuracy and action recognition by ~11\% on average on OpenVID-1M (relative), while maintaining visual quality. User studies show 74--83% preference for our physics-enhanced videos. It also reduces warping error by 22--37% (depending on the backbone) and improves temporal consistency scores. These results indicate that simple, global spectral cues are an effective drop-in regular-
izer for physically plausible motion in video diffusion.