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Example-guided image synthesis has recently been attempted to synthesize animage from a semantic label map and an exemplary image. In the task, theadditional exemplar image provides the style guidance that controls theappearance of the synthesized output. Despite the controllability advantage,the existing models are designed on datasets with specific and roughly alignedobjects. In this paper, we tackle a more challenging and general task, wherethe exemplar is an arbitrary scene image that is semantically different fromthe given label map. To this end, we first propose a Masked Spatial-ChannelAttention (MSCA) module which models the correspondence between two arbitraryscenes via efficient decoupled attention. Next, we propose an end-to-endnetwork for joint global and local feature alignment and synthesis. Finally, wepropose a novel self-supervision task to enable training. Experiments on thelarge-scale and more diverse COCO-stuff dataset show significant improvementsover the existing methods. Moreover, our approach provides interpretability andcan be readily extended to other content manipulation tasks including style andspatial interpolation or extrapolation.

Example-Guided Image Synthesis across Arbitrary Scenes using Masked Spatial-Channel Attention and Self-Supervision