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MaskFlow introduces a novel training framework that integrates masks directly into flow-matching objectives to achieve precise localization and seamless boundaries in regional image editing. By incorporating a Soft-Poisson De-seaming module, the method effectively refines vector fields during sampling to ensure natural integration between edited foregrounds and preserved backgrounds. This advancement addresses critical challenges in spatial controllability for generative models, offering improved performance over existing instruction-based and mask-based techniques.
MaskFlow: Precise, Consistent and Seamless Regional Image Editing
arXiv:2608.06929v3 Announce Type: replace-cross Abstract: 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
Abstract: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: this https URL
26 pages, 17 figures. Project page: this https URL
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2608.06929 [cs.CV]
(or arXiv:2608.06929v3 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.06929 Focus to learn more arXiv-issued DOI via DataCite
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