Introducing
Measuring Physical Frame Rate
from Visual Dynamics.
Texas A&M University
Modern video generators produce stunning visuals — yet they lack a reliable internal clock. A hummingbird might flap in extreme slow motion. A falling person might defy gravity. We call this Chronometric Hallucination.
Visual Chronometer recovers the true Physical FPS directly from visual motion, enabling corrections that dramatically improve perceived naturalness.
Original AI-generated videos vs. PhyFPS-corrected versions. Users overwhelmingly preferred the corrected version in every case.
A Pomeranian dog chasing a soccer ball across a lawn.
24 fps → 35.8 fps
A snake slithering across polished wooden floorboards.
24 fps → 60.2 fps
A detailed view of the churning white wake trailing behind a large ship.
16 fps → 44.9 fps
A continuous tracking shot moving steadily through a brightly lit subway tunnel.
16 fps → 36.7 fps
Martial arts students performing synchronized stretching exercises.
24 fps → 51.5 fps
A chef tossing a crab in a flaming wok filled with hot oil.
24 fps → 49.8 fps
Onion rings frying in bubbling hot oil.
24 fps → 58.9 fps
A chameleon shooting its tongue out to catch an ant.
24 fps → 52.5 fps
Captured fish struggling inside a fishing net.
24 fps → 15.0 fps
Raindrops falling and hitting green leaves.
16 fps → 21.1 fps
State-of-the-art generators exhibit large gaps between nominal frame rate and actual physical motion speed.
Physical speed fluctuates both across prompts and within individual videos — even under identical settings.
Re-timing to predicted PhyFPS significantly improves human-perceived naturalness in controlled user studies.
General-purpose Vision-Language Models are unreliable temporal judges — a dedicated predictor is essential.
@article{gao2026visual_chronometer,
title = {The Pulse of Motion: Measuring Physical Frame Rate
from Visual Dynamics},
author = {Gao, Xiangbo and Wu, Mingyang and Yang, Siyuan
and Yu, Jiongze and Taghavi, Pardis and Lin, Fangzhou
and Tu, Zhengzhong},
year = {2026}
}