Add model card for Visual Chronometer

This PR improves the model card for the Visual Chronometer model. It adds:
- The `video-classification` pipeline tag.
- Links to the paper, project page, and GitHub repository.
- Installation and sample usage instructions for predicting PhyFPS.

README.md

@@ -1,3 +1,47 @@
----
-license: apache-2.0
----
+---
+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](https://huggingface.co/papers/2603.14375)
+- **Project Page:** [https://xiangbogaobarry.github.io/Pulse-of-Motion/](https://xiangbogaobarry.github.io/Pulse-of-Motion/)
+- **Repository:** [https://github.com/taco-group/Pulse-of-Motion](https://github.com/taco-group/Pulse-of-Motion)
+
+## Installation
+
+```bash
+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
+
+```bash
+cd inference
+python predict.py --video_path path/to/your_video.mp4
+```
+
+### Predict PhyFPS for a directory of videos
+
+```bash
+cd inference
+python predict.py --video_dir path/to/videos/ --output_csv results.csv
+```
+
+## Citation
+
+```bibtex
+@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}
+}
+```