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Example-guided image synthesis has been recently attempted to synthesize animage from a semantic label map and an exemplary image. In the task, theadditional exemplary image serves to provide style guidance that controls theappearance of the synthesized output. Despite the controllability advantage,the previous 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 unaligned to thegiven label map. To this end, we first propose a new Masked Spatial-ChannelAttention (MSCA) module which models the correspondence between twounstructured scenes via cross-attention. Next, we propose an end-to-end networkfor joint global and local feature alignment and synthesis. In addition, wepropose a novel patch-based self-supervision scheme to enable training.Experiments on the large-scale CCOO-stuff dataset show significant improvementsover existing methods. Moreover, our approach provides interpretability and canbe readily extended to other tasks including style and spatial interpolation orextrapolation, as well as other content manipulation.

Example-Guided Scene Image Synthesis using Masked Spatial-Channel Attention and Patch-Based Self-Supervision