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Semantic image editing provides users with a flexible tool to modify a givenimage guided by a corresponding segmentation map. In this task, the features ofthe foreground objects and the backgrounds are quite different. However, allprevious methods handle backgrounds and objects as a whole using a monolithicmodel. Consequently, they remain limited in processing content-rich images andsuffer from generating unrealistic objects and texture-inconsistentbackgrounds. To address this issue, we propose a novel paradigm,\textbf{S}emantic \textbf{I}mage \textbf{E}diting by \textbf{D}isentangling\textbf{O}bject and \textbf{B}ackground (\textbf{SIEDOB}), the core idea ofwhich is to explicitly leverages several heterogeneous subnetworks for objectsand backgrounds. First, SIEDOB disassembles the edited input into backgroundregions and instance-level objects. Then, we feed them into the dedicatedgenerators. Finally, all synthesized parts are embedded in their originallocations and utilize a fusion network to obtain a harmonized result. Moreover,to produce high-quality edited images, we propose some innovative designs,including Semantic-Aware Self-Propagation Module, Boundary-Anchored PatchDiscriminator, and Style-Diversity Object Generator, and integrate them intoSIEDOB. We conduct extensive experiments on Cityscapes and ADE20K-Room datasetsand exhibit that our method remarkably outperforms the baselines, especially insynthesizing realistic and diverse objects and texture-consistent backgrounds.

SIEDOB: Semantic Image Editing by Disentangling Object and Background