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We develop a new method for portrait image editing, which supportsfine-grained editing of geometries, colors, lights and shadows using a singleneural network model. We adopt a novel asymmetric conditional GAN architecture:the generators take the transformed conditional inputs, such as edge maps,color palette, sliders and masks, that can be directly edited by the user; thediscriminators take the conditional inputs in the way that can guidecontrollable image generation more effectively. Taking color editing as anexample, we feed color palettes (which can be edited easily) into thegenerator, and color maps (which contain positional information of colors) intothe discriminator. We also design a region-weighted discriminator so thathigher weights are assigned to more important regions, like eyes and skin.Using a color palette, the user can directly specify the desired colors ofhair, skin, eyes, lip and background. Color sliders allow the user to blendcolors in an intuitive manner. The user can also edit lights and shadows bymodifying the corresponding masks. We demonstrate the effectiveness of ourmethod by evaluating it on the CelebAMask-HQ dataset with a wide range oftasks, including geometry/color/shadow/light editing, hand-drawn sketch toimage translation, and color transfer. We also present ablation studies tojustify our design.

Flexible Portrait Image Editing with Fine-Grained Control