Add model card metadata and links for Learning to Refocus with Video Diffusion Models

README.md

@@ -1,6 +1,28 @@
 ---
-# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
-# Doc / guide: https://huggingface.co/docs/hub/model-cards
-{}
+pipeline_tag: image-to-video
+library_name: diffusers
 ---
-https://huggingface.co/papers/2512.19823
+
+# Learning to Refocus with Video Diffusion Models
+
+This repository contains the model weights for the paper [Learning to Refocus with Video Diffusion Models](https://huggingface.co/papers/2512.19823).
+
+[**Project Page**](https://learn2refocus.github.io/) | [**GitHub Repository**](https://github.com/tedlasai/learn2refocus)
+
+## Summary
+Focus is a cornerstone of photography, yet autofocus systems often fail to capture the intended subject, and users frequently wish to adjust focus after capture. This work introduces a novel method for realistic post-capture refocusing using video diffusion models. From a single defocused image, the approach generates a perceptually accurate focal stack, represented as a video sequence, enabling interactive refocusing and unlocking a range of downstream applications.
+
+## Usage
+For detailed environment setup, training, and testing instructions, please refer to the official [GitHub repository](https://github.com/tedlasai/learn2refocus). The model utilizes fine-tuned Stable Video Diffusion (SVD) weights.
+
+## Citation
+If you use our dataset, code, or model in your research, please cite the following paper:
+
+```bibtex
+@inproceedings{Tedla2025Refocus,
+  title={{Learning to Refocus with Video Diffusion Models}},
+  author={{Tedla, SaiKiran and Zhang, Zhoutong and Zhang, Xuaner and Xin, Shumian}},
+  booktitle={{Proceedings of the ACM SIGGRAPH Asia Conference}},
+  year={{2025}}
+}
+```