Image Synthesis via Semantic Composition

Yi Wang, Lu Qi, Ying-Cong Chen, Xiangyu Zhang, Jiaya Jia; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 13749-13758


In this paper, we present a novel approach to synthesize realistic images based on their semantic layouts. It hypothesizes that for objects with similar appearance, they share similar representation. Our method establishes dependencies between regions according to their appearance correlation, yielding both spatially variant and associated representations. Conditioning on these features, we propose a dynamic weighted network constructed by spatially conditional computation (with both convolution and normalization). More than preserving semantic distinctions, the given dynamic network strengthens semantic relevance, benefiting global structure and detail synthesis. We demonstrate that our method gives the compelling generation performance qualitatively and quantitatively with extensive experiments on benchmarks.

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[pdf] [supp] [arXiv]
@InProceedings{Wang_2021_ICCV, author = {Wang, Yi and Qi, Lu and Chen, Ying-Cong and Zhang, Xiangyu and Jia, Jiaya}, title = {Image Synthesis via Semantic Composition}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {13749-13758} }