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arXiv 提交日期: 2025-12-22
📄 Abstract - Learning to Refocus with Video Diffusion Models

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. We introduce a novel method for realistic post-capture refocusing using video diffusion models. From a single defocused image, our approach generates a perceptually accurate focal stack, represented as a video sequence, enabling interactive refocusing and unlocking a range of downstream applications. We release a large-scale focal stack dataset acquired under diverse real-world smartphone conditions to support this work and future research. Our method consistently outperforms existing approaches in both perceptual quality and robustness across challenging scenarios, paving the way for more advanced focus-editing capabilities in everyday photography. Code and data are available at this https URL

顶级标签: computer vision video generation aigc
详细标签: video diffusion models focal stack generation post-capture refocusing image editing photography enhancement 或 搜索:

利用视频扩散模型学习重聚焦 / Learning to Refocus with Video Diffusion Models


1️⃣ 一句话总结

这项研究提出了一种新方法,能够仅凭一张模糊的照片,利用视频扩散模型生成一系列不同焦点的清晰图像,从而实现拍照后灵活调整焦点,并发布了相关数据集以支持未来研究。

源自 arXiv: 2512.19823