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Despite the progress in semantic image synthesis, it remains a challengingproblem to generate photo-realistic parts from input semantic map. Integratingpart segmentation map can undoubtedly benefit image synthesis, but isbothersome and inconvenient to be provided by users. To improve part synthesis,this paper presents to infer Parts from Object ShapE (iPOSE) and leverage itfor improving semantic image synthesis. However, albeit several partsegmentation datasets are available, part annotations are still not providedfor many object categories in semantic image synthesis. To circumvent it, weresort to few-shot regime to learn a PartNet for predicting the object part mapwith the guidance of pre-defined support part maps. PartNet can be readilygeneralized to handle a new object category when a small number (e.g., 3) ofsupport part maps for this category are provided. Furthermore, part semanticmodulation is presented to incorporate both inferred part map and semantic mapfor image synthesis. Experiments show that our iPOSE not only generates objectswith rich part details, but also enables to control the image synthesisflexibly. And our iPOSE performs favorably against the state-of-the-art methodsin terms of quantitative and qualitative evaluation. Our code will be publiclyavailable at https://github.com/csyxwei/iPOSE.

Inferring and Leveraging Parts from Object Shape for Improving Semantic Image Synthesis