MaskFlow: Precise, Consistent and Seamless Regional Image Editing
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...
| Authors | Rui Xu, Yang Yong, Shunzi Yang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 7 August 2026 |
| Last updated at source | 28 September 2026 |
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- ReyChiaro/MaskEdit-10kDataset · cited by
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- ReyChiaro/MaskEdit-10kDataset
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