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Recent facial image synthesis methods have been mainly based on conditionalgenerative models. Sketch-based conditions can effectively describe thegeometry of faces, including the contours of facial components, hairstructures, as well as salient edges (e.g., wrinkles) on face surfaces but lackeffective control of appearance, which is influenced by color, material,lighting condition, etc. To have more control of generated results, onepossible approach is to apply existing disentangling works to disentangle faceimages into geometry and appearance representations. However, existingdisentangling methods are not optimized for human face editing, and cannotachieve fine control of facial details such as wrinkles. To address this issue,we propose DeepFaceEditing, a structured disentanglement framework specificallydesigned for face images to support face generation and editing withdisentangled control of geometry and appearance. We adopt a local-to-globalapproach to incorporate the face domain knowledge: local component images aredecomposed into geometry and appearance representations, which are fusedconsistently using a global fusion module to improve generation quality. Weexploit sketches to assist in extracting a better geometry representation,which also supports intuitive geometry editing via sketching. The resultingmethod can either extract the geometry and appearance representations from faceimages, or directly extract the geometry representation from face sketches.Such representations allow users to easily edit and synthesize face images,with decoupled control of their geometry and appearance. Both qualitative andquantitative evaluations show the superior detail and appearance controlabilities of our method compared to state-of-the-art methods.

DeepFaceEditing: Deep Face Generation and Editing with Disentangled Geometry and Appearance Control