icon Generative Refocusing
Flexible Defocus Control from a Single Image

Chun-Wei Tuan Mu1 Jia-Bin Huang2 Yu-Lun Liu1
1National Yang Ming Chiao Tung University 2University of Maryland, College Park
arXiv Video HF Demo Code

Given a single input image, our method enables flexible refocusing and aperture adjustment.
The heatmap overlay visualizes the synthesized defocus map.

Historical Images

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In-the-wild Images

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Interactive Refocusing

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Generative refocusing demo
Click on a subject to refocus
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Comparison with Gemini 3 (Nano Banana Pro)

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Method

Our method decomposes single-image refocusing into two stages: (a) Defocus Deblurring and (b) Shallow Depth-of-Field Synthesis. Given a blurry input image Iin, we optionally apply a pre-deblurring to obtain a conservative estimate Isyn, then feed both Iin and Isyn into DeblurNet to recover a high-quality all-in-focus image Iaif. In the second stage, BokehNet takes the all-in-focus image Iaif, the defocus map Ddef, and optionally a specific aperture shape as inputs to synthesize the refocused output.
Method Pipeline

Visual Comparison

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Visual Comparison for BokehNet Training Ablation

Training solely on synthetic data tends to result in an incorrect depth scale, leading to excessive bokeh strength and noticeable artifacts along object boundaries.
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Failure Cases

For severely blurred inputs, the model may hallucinate incorrect details (e.g., reconstructing "Jewelry shop" as "Temnlry shop").
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Related Links

There has been great works on refocusing or bokeh that were done concurrently by other researchers, feel free to check them out!

BibTeX

@article{Genfocus2025,
          title     = {Generative Refocusing: Flexible Defocus Control from a Single Image},
          author    = {Tuan Mu, Chun-Wei and Huang, Jia-Bin and Liu, Yu-Lun},
          journal   = {arXiv preprint arXiv:2512.16923},
          year      = {2025}
        }