Generative Refocusing: Flexible Defocus Control from a Single Image

Depth-of-field control is essential in photography, but achieving perfect focus often requires multiple attempts or specialized equipment. Single-image refocusing is still difficult, requiring both sharp content recovery and realistic bokeh synthesis. Current methods have significant drawbacks: they require all-in-focus inputs, rely on synthetic data from simulators, and offer limited control over the aperture. We introduce Generative Refocusing, built around a training scheme that completes missing signals, converting synthetic renderings, single real bokeh photographs, and paired real captures into a unified training format. This allows us to scale training jointly across heterogeneous real-world sources that prior methods could not combine, letting our model learn authentic optical characteristics beyond what simulators capture while enabling precise control over focus and bokeh intensity. Our experiments show we achieve state-of-the-art performance in defocus deblurring, bokeh synthesis, and refocusing benchmarks.