MotionPhys: Detecting AI-Generated Videos via Physical Consistency of Optical-Flow Trajectories

2026-08-21

AI Summary

针对AI生成视频中物理运动一致性问题，提出MotionPhys框架，通过分析光流轨迹的几何演变来检测视频中的物理不一致。

Abstract

Modern AI video generation models can produce videos with high visual fidelity and seemingly smooth temporal transitions. However, visual realism does not necessarily imply physical motion consistency. Existing generative models mainly optimize distribution matching in pixel or latent spaces, without explicitly enforcing real-world constraints such as inertia, continuous forces, and trajectory geometry. Our experiments show that AI-generated videos remain visually plausible over short sequences of consecutive frames, yet fail to preserve physical motion consistency throughout a complete object action, resulting in systematic statistical discrepancies in their motion trajectories. Based on this observation, we introduce MotionPhys, a lightweight and interpretable framework that treats sparse motion trajectories as physical evidence rather than relying on appearance artifacts or generator-specific traces. By modeling the geometric evolution of trajectories across multiple temporal scales, MotionPhys reveals subtle motion inconsistencies that are difficult to capture with conventional visual cues and transforms them into a compact representation for efficient detection. Experiments on multiple datasets show that MotionPhys can effectively detect physical inconsistencies in generated videos and generalizes well across different video generators.

Problem

• physical consistency
• AI-generated videos
• motion trajectories

Innovation

• Physical Consistency
• Optical-Flow Trajectories
• Sparse Motion Trajectories
• Temporal Scales
• Compact Representation