MaskFlow：精准、一致且无缝的区域图像编辑
MaskFlow: Precise, Consistent and Seamless Regional Image Editing
浏览论文内容
中文总结 AI 辅助
MaskFlow框架通过融合掩码的流匹配目标与Soft-Poisson去接缝模块，结合MEData数据集，实现了精准可控、无缝融合的区域图像编辑，性能优于现有方法。
中文摘要 AI 辅助
区域图像编辑因其空间可控性已受到广泛关注。尽管基于指令和基于掩码参考的编辑方法能实现较强的语义对齐，但可靠的区域控制仍具挑战性，编辑操作必须准确定位并与保留的上下文自然融合。我们提出MaskFlow，这是一个用于精准定位、一致背景保留和无缝边界过渡的训练框架。MaskFlow将掩码融入概率路径和流匹配目标，协调可编辑区域内的生成与区域外的源保留。我们还设计了Soft-Poisson去接缝模块，在训练和采样过程中优化预测向量场，提升编辑前景与保留背景的平滑融合效果。此外，我们构建了MEData，一个基于掩码的图像编辑数据集，用于训练区域图像编辑模型并推动相关研究。在自然场景和信息图图像上的实验表明，该方法在定量和定性评估中均优于对比方法。
英文摘要
Regional image editing has attracted considerable attention for its spatial controllability. Although instruction-based and mask-based editing methods can achieve strong semantic alignment, reliable regional control remains challenging, where an edit must be accurately localized and naturally integrated with the preserved context. We propose MaskFlow, a training framework for precise localization, consistent background preservation, and seamless boundary transitions. MaskFlow incorporates the mask into the probability path and flow-matching objective, coordinating generation within the editable region with source preservation outside it. The proposed Soft-Poisson De-seaming module further refines the predicted vector field during both training and sampling to improve the smooth integration of the edited foreground with the preserved background. We also introduce MaskEdit-Benchmark for general scene and infographics editing, where prompts describe the desired edits without localization cues, leaving masks to specify the target regions. Experiments on natural scenes and infographic images demonstrate consistent improvements over competing methods in both quantitative and qualitative evaluations. Project page: https://reychiaro.github.io/MaskFlow
发表机构
- SenseTime Research（商汤科技研究院）
- Beihang University（北京航空航天大学）
- Nanyang Technological University（南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。
