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Semantic image synthesis aims at generating photorealistic images fromsemantic layouts. Previous approaches with conditional generative adversarialnetworks (GAN) show state-of-the-art performance on this task, which eitherfeed the semantic label maps as inputs to the generator, or use them tomodulate the activations in normalization layers via affine transformations. Weargue that convolutional kernels in the generator should be aware of thedistinct semantic labels at different locations when generating images. Inorder to better exploit the semantic layout for the image generator, we proposeto predict convolutional kernels conditioned on the semantic label map togenerate the intermediate feature maps from the noise maps and eventuallygenerate the images. Moreover, we propose a feature pyramid semantics-embeddingdiscriminator, which is more effective in enhancing fine details and semanticalignments between the generated images and the input semantic layouts thanprevious multi-scale discriminators. We achieve state-of-the-art results onboth quantitative metrics and subjective evaluation on various semanticsegmentation datasets, demonstrating the effectiveness of our approach.

Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis