Datensatz

Zusammenfassung

StyleGAN generates photorealistic portrait images of faces with eyes, teeth,
hair and context (neck, shoulders, background), but lacks a rig-like control
over 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 lack
photorealism when rendered and only model the face interior, not other parts of
a portrait image (hair, mouth interior, background). We present the first
method to provide a face rig-like control over a pretrained and fixed StyleGAN
via a 3DMM. A new rigging network, RigNet is trained between the 3DMM's
semantic parameters and StyleGAN's input. The network is trained in a
self-supervised manner, without the need for manual annotations. At test time,
our method generates portrait images with the photorealism of StyleGAN and
provides explicit control over the 3D semantic parameters of the face.