metadata
license: apache-2.0
pipeline_tag: video-classification
Visual Chronometer
Visual Chronometer is a model that predicts the Physical Frames Per Second (PhyFPS) of a video — the true temporal scale implied by its visual motion, independent of container metadata.
- Paper: The Pulse of Motion: Measuring Physical Frame Rate from Visual Dynamics
- Project Page: https://xiangbogaobarry.github.io/Pulse-of-Motion/
- Repository: https://github.com/taco-group/Pulse-of-Motion
Installation
git clone https://github.com/taco-group/Pulse-of-Motion.git
cd Pulse-of-Motion/inference
pip install -r requirements.txt
Usage
Predict PhyFPS for a single video
cd inference
python predict.py --video_path path/to/your_video.mp4
Predict PhyFPS for a directory of videos
cd inference
python predict.py --video_dir path/to/videos/ --output_csv results.csv
Citation
@article{gao2026pulse,
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},
journal={arXiv preprint arXiv:2603.14375},
year={2026}
}