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StyleGAN generates photorealistic portrait images of faces with eyes, teeth,hair and context (neck, shoulders, background), but lacks a rig-like controlover semantic face parameters that are interpretable in 3D, such as face pose,expressions, and scene illumination. Three-dimensional morphable face models(3DMMs) on the other hand offer control over the semantic parameters, but lackphotorealism when rendered and only model the face interior, not other parts ofa portrait image (hair, mouth interior, background). We present the firstmethod to provide a face rig-like control over a pretrained and fixed StyleGANvia a 3DMM. A new rigging network, RigNet is trained between the 3DMM'ssemantic parameters and StyleGAN's input. The network is trained in aself-supervised manner, without the need for manual annotations. At test time,our method generates portrait images with the photorealism of StyleGAN andprovides explicit control over the 3D semantic parameters of the face.

StyleRig: Rigging StyleGAN for 3D Control over Portrait Images