PhysPO: Physics-Aware Local Preference Optimization for Physically Consistent Video Diffusion

Despite rapid progress in video diffusion models (VDMs),ensuring semantic adherence and physical commonsense remains a fun-damental challenge, as even state-of-the-art systems frequently violatereal-world physical laws, such as dynamics, thermodynamics, and op-tics. While Direct Preference Optimization (DPO) has become a popu-lar post-training strategy for aligning generative models, existing videoDPO methods to improve physical commonsense suffer from high compu-tational costs, poorly matched preference pairs, and ambiguous global su-pervision that fails to localize physical violations. We propose PhysPO,a physics-aware local preference optimization framework for physicallyconsistent video generation. We first introduce CounterPhyPipe, a physics-aware counterfactual data construction pipeline that forms video pref-erence pairs with consistent global semantics and local physical viola-tions, enabling meaningful comparisons for preference learning. Thenwe leverage high-frequency decomposition to introduce a physical mask,which localizes regions where physical state transitions may occur. Fi-nally, we develop PhysPO, a physics-aware DPO framework that con-centrates supervision on physically relevant regions while enforcing neu-trality on background regions. This mechanism reduces gradient noiseand mitigates shortcut optimization, encouraging the model to focus ongenuine physical discrepancies rather than superficial cues. Extensiveexperiments demonstrate that PhysPO significantly improves physicalcommonsense without compromising semantic adherence. Our projectpage is here3 .