BokehFlow: Depth-Free Controllable Bokeh Rendering via Flow Matching
This addresses a challenge in computational photography for enhancing visual aesthetics in images, though it is incremental as it builds on flow matching techniques.
The paper tackles the problem of rendering controllable bokeh effects without requiring depth inputs, achieving photorealistic results and precise semantic control via text prompts, outperforming existing methods in quality and efficiency.
Bokeh rendering simulates the shallow depth-of-field effect in photography, enhancing visual aesthetics and guiding viewer attention to regions of interest. Although recent approaches perform well, rendering controllable bokeh without additional depth inputs remains a significant challenge. Existing classical and neural controllable methods rely on accurate depth maps, while generative approaches often struggle with limited controllability and efficiency. In this paper, we propose BokehFlow, a depth-free framework for controllable bokeh rendering based on flow matching. BokehFlow directly synthesizes photorealistic bokeh effects from all-in-focus images, eliminating the need for depth inputs. It employs a cross-attention mechanism to enable semantic control over both focus regions and blur intensity via text prompts. To support training and evaluation, we collect and synthesize four datasets. Extensive experiments demonstrate that BokehFlow achieves visually compelling bokeh effects and offers precise control, outperforming existing depth-dependent and generative methods in both rendering quality and efficiency.