The Pulse of Motion: Measuring Physical Frame Rate from Visual Dynamics
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/.
69c570ea2de2ee20094ed991
Xiangbo Gao
69c570ea2de2ee20094ed992
Mingyang Wu
69c570ea2de2ee20094ed993
Siyuan Yang
69c570ea2de2ee20094ed994
Jiongze Yu
claimed_verified
2026-04-07T08:47:28.190Z
67fe57528a7ce1baa8a42ca8
https://cdn-avatars.huggingface.co/v1/production/uploads/67fe57528a7ce1baa8a42ca8/KM41bfgws4JsnLp303T4Y.png
JiongzeYu
JiongzeYu
user
JiongzeYu
69c570ea2de2ee20094ed995
Pardis Taghavi
69c570ea2de2ee20094ed996
Fangzhou Lin
69c570ea2de2ee20094ed997
Zhengzhong Tu
The Pulse of Motion: Measuring Physical Frame Rate from Visual Dynamics
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/.
Generative video models suffer from temporal ambiguity due to inconsistent frame rate training, which this work addresses through a Visual Chronometer that estimates physical frames per second from visual dynamics.
generative video models world models physical simulation temporal ambiguity chronometric hallucination Visual Chronometer Physical Frames Per Second PhyFPS temporal resampling video generation benchmarking