无需深度图，用文本控制模糊区域和强度，生成逼真虚化效果。
BokehFlow: Depth-Free Controllable Bokeh Rendering via Flow Matching
- 基于流匹配，直接从全焦图像生成虚化效果。
- 支持文本指令精确控制焦点区域与模糊程度，效果更自然。
- 适合需要快速、灵活虚化编辑的视觉设计与摄影应用。
虚化渲染可模拟摄影中的浅景深效果，提升视觉美感并引导观众注意力至关注区域。尽管近期方法表现良好，但在无额外深度输入的情况下实现可控虚化仍具挑战。传统与神经方法依赖准确的深度图，而生成式方法常受限于控制力与效率。本文提出BokehFlow，一种基于流匹配的无深度可控虚化框架。该方法直接从全焦图像合成逼真虚化效果，无需深度输入。通过交叉注意力机制，可利用文本提示对焦点区域和模糊强度进行语义控制。为支持训练与评估，我们构建并合成四个数据集。大量实验表明，BokehFlow在渲染质量与效率上均优于现有依赖深度或生成式方法，实现视觉上令人信服的虚化效果与精准控制。
原文摘要 · Abstract (English)
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.
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