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The research topic of sketch-to-portrait generation has witnessed a boost ofprogress with deep learning techniques. The recently proposed StyleGANarchitectures achieve state-of-the-art generation ability but the originalStyleGAN is not friendly for sketch-based creation due to its unconditionalgeneration nature. To address this issue, we propose a direct conditioningstrategy to better preserve the spatial information under the StyleGANframework. Specifically, we introduce Spatially Conditioned StyleGAN(SC-StyleGAN for short), which explicitly injects spatial constraints to theoriginal StyleGAN generation process. We explore two input modalities, sketchesand semantic maps, which together allow users to express desired generationresults more precisely and easily. Based on SC-StyleGAN, we presentDrawingInStyles, a novel drawing interface for non-professional users to easilyproduce high-quality, photo-realistic face images with precise control, eitherfrom scratch or editing existing ones. Qualitative and quantitative evaluationsshow the superior generation ability of our method to existing and alternativesolutions. The usability and expressiveness of our system are confirmed by auser study.

DrawingInStyles: Portrait Image Generation and Editing with Spatially Conditioned StyleGAN