Add model card metadata and links for Learning to Refocus with Video Diffusion Models (#2)
Browse files- Add model card metadata and links for Learning to Refocus with Video Diffusion Models (3189c45ec3509ff8ac64d10562a468dfeb64f4eb)
Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>
README.md
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pipeline_tag: image-to-video
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library_name: diffusers
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# Learning to Refocus with Video Diffusion Models
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This repository contains the model weights for the paper [Learning to Refocus with Video Diffusion Models](https://huggingface.co/papers/2512.19823).
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[**Project Page**](https://learn2refocus.github.io/) | [**GitHub Repository**](https://github.com/tedlasai/learn2refocus)
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## Summary
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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.
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## Usage
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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.
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## Citation
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If you use our dataset, code, or model in your research, please cite the following paper:
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```bibtex
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@inproceedings{Tedla2025Refocus,
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title={{Learning to Refocus with Video Diffusion Models}},
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author={{Tedla, SaiKiran and Zhang, Zhoutong and Zhang, Xuaner and Xin, Shumian}},
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booktitle={{Proceedings of the ACM SIGGRAPH Asia Conference}},
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year={{2025}}
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}
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```
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