  We develop a new method for portrait image editing, which supports
fine-grained editing of geometries, colors, lights and shadows using a single
neural 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; the
discriminators take the conditional inputs in the way that can guide
controllable image generation more effectively. Taking color editing as an
example, we feed color palettes (which can be edited easily) into the
generator, and color maps (which contain positional information of colors) into
the discriminator. We also design a region-weighted discriminator so that
higher weights are assigned to more important regions, like eyes and skin.
Using a color palette, the user can directly specify the desired colors of
hair, skin, eyes, lip and background. Color sliders allow the user to blend
colors in an intuitive manner. The user can also edit lights and shadows by
modifying the corresponding masks. We demonstrate the effectiveness of our
method by evaluating it on the CelebAMask-HQ dataset with a wide range of
tasks, including geometry/color/shadow/light editing, hand-drawn sketch to
image translation, and color transfer. We also present ablation studies to
justify our design.
