The Pulse of Motion: Measuring Physical Frame Rate from Visual Dynamics

TL;DR

This paper introduces Visual Chronometer, a method to estimate the true physical frame rate from video dynamics, addressing temporal inconsistencies in generative models and improving video realism.

Contribution

It proposes a novel predictor for recovering physical frame rate directly from visual motion, along with benchmarks to evaluate temporal stability in video generation.

Findings

State-of-the-art generators suffer from severe PhyFPS misalignment.

Applying PhyFPS correction improves perceived naturalness of AI-generated videos.

The method accurately estimates true temporal scale from motion cues.

Abstract

While recent generative video models have achieved remarkable visual realism and are being explored as world models, true physical simulation requires mastering both space and time. Current models can produce visually smooth kinematics, yet they lack a reliable internal motion pulse to ground these motions in a consistent, real-world time scale. This temporal ambiguity stems from the common practice of indiscriminately training on videos with vastly different real-world speeds, forcing them into standardized frame rates. This leads to what we term chronometric hallucination: generated sequences exhibit ambiguous, unstable, and uncontrollable physical motion speeds. To address this, we propose Visual Chronometer, a predictor that recovers the Physical Frames Per Second (PhyFPS) directly from the visual dynamics of an input video. Trained via controlled temporal resampling, our method estimates the true temporal scale implied by the motion itself, bypassing unreliable metadata. To systematically quantify this issue, we establish two benchmarks, PhyFPS-Bench-Real and PhyFPS-Bench-Gen. Our evaluations reveal a harsh reality: state-of-the-art video generators suffer from severe PhyFPS misalignment and temporal instability. Finally, we demonstrate that applying PhyFPS corrections significantly improves the human-perceived naturalness of AI-generated videos. Our project page is https://xiangbogaobarry.github.io/Visual_Chronometer/.