MaskFlow: Precise, Consistent and Seamless Regional Image Editing

2026-08-07

AI Summary

This work addresses the challenges of region-based image editing—namely precise spatial localization, preservation of background consistency, and seamless boundary blending—by introducing MaskFlow, a novel framework that integrates mask information into the flow-matching generative process for the first time. MaskFlow employs mask-guided probabilistic paths to jointly regulate content generation within editable regions and preservation outside them. Furthermore, it incorporates a Soft-Poisson de-blending module to refine the vector field, enabling natural fusion between foreground and background. Coupled with a mask-driven MEData synthesis strategy, the proposed method consistently outperforms existing approaches on both natural images and infographics, with quantitative and qualitative results demonstrating superior performance in editing accuracy, background fidelity, and boundary seamlessness.

Abstract

Regional image editing has attracted considerable attention for its spatial controllability. Although instruction-based and mask-reference-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 \textbf{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 design a data synthesis pipeline to construct MEData, a mask-based image editing dataset for training regional image editing models and facilitating further research. Experiments on natural scenes and infographic images demonstrate consistent improvements over competing methods in both quantitative and qualitative evaluations.

Problem

• regional image editing
• precise localization
• background preservation
• seamless boundary
• spatial controllability

Innovation

• MaskFlow
• flow matching
• regional image editing
• Soft-Poisson de-seaming
• MEData