Intuitive User-Guided Portrait Image Editing with Asymmetric Conditional GAN

We propose PortraitACG, a novel framework for user-guided portrait image editing that leverages an asymmetric conditional generative adversarial network (GAN), which supports the fine-grained editing of geometries, colors, lights, and shadows using a single neural network model. Existing conditional GAN-based approaches usually feed the same conditional information into generators and discriminators, which is sub-optimal because these two modules are designed for different purposes. To facilitate flexible user-guided editing, we propose a novel asymmetric conditional GAN, where the generators take the transformed conditional inputs, such as edge maps, color palettes, sliders, and masks, that can be directly edited by the user, and the discriminators take the conditional inputs in a way that can guide controllable image generation more effectively. This allows image editing operations to be performed in a simpler and more intuitive manner. For example, the user can directly use a color palette to specify the desired colors of hair, skin, eyes, lips, and background and use a slider to blend colors. Moreover, users can edit the lights and shadows by modifying their corresponding masks.