PhyRPR: Training-Free Physics-Constrained Video Generation
Recent diffusion-based video generation models can synthesize visually plausible videos, yet they often struggle to satisfy physical constraints. A key reason is that most existing approaches remain single-stage: they entangle high-level physical understanding with low-level visual synthesis, making it hard to generate content that require explicit physical reasoning. To address this limitation, we propose a training-free three-stage pipeline,PhyRPR:Phy\uline{Reason}--Phy\uline{Plan}--Phy\uline{Refine}, which decouples physical understanding from visual synthesis. Specifically, PhyReason uses a large multimodal model for physical state reasoning and an image generator for keyframe synthesis; PhyPlan deterministically synthesizes a controllable coarse motion scaffold; and PhyRefine injects this scaffold into diffusion sampling via a latent fusion strategy to refine appearance while preserving the planned dynamics. This staged design enables explicit physical control during generation. Extensive experiments under physics constraints show that our method consistently improves physical plausibility and motion controllability.
696a064a844a787c4fdea458
Yibo Zhao
claimed_verified
2026-01-16T10:28:24.372Z
63710c2bafbe42caa5a4dfc8
/avatars/33fd1695466bff2b14c3ab42793826ad.svg
zhaoyibo
zjuyb
user
zjuyb
696a064a844a787c4fdea459
Hengjia Li
696a064a844a787c4fdea45a
Xiaofei He
696a064a844a787c4fdea45b
Boxi Wu
PhyRPR: Training-Free Physics-Constrained Video Generation
Recent diffusion-based video generation models can synthesize visually plausible videos, yet they often struggle to satisfy physical constraints. A key reason is that most existing approaches remain single-stage: they entangle high-level physical understanding with low-level visual synthesis, making it hard to generate content that require explicit physical reasoning. To address this limitation, we propose a training-free three-stage pipeline,PhyRPR:Phy\uline{Reason}--Phy\uline{Plan}--Phy\uline{Refine}, which decouples physical understanding from visual synthesis. Specifically, PhyReason uses a large multimodal model for physical state reasoning and an image generator for keyframe synthesis; PhyPlan deterministically synthesizes a controllable coarse motion scaffold; and PhyRefine injects this scaffold into diffusion sampling via a latent fusion strategy to refine appearance while preserving the planned dynamics. This staged design enables explicit physical control during generation. Extensive experiments under physics constraints show that our method consistently improves physical plausibility and motion controllability.
A three-stage pipeline decouples physical reasoning from visual synthesis in video generation, improving physical plausibility and motion controllability through distinct phases of reasoning, planning, and refinement.
diffusion models video generation physical reasoning motion scaffolding latent fusion strategy