  StyleGAN has achieved great progress in 2D face reconstruction and semantic
editing via image inversion and latent editing. While studies over extending 2D
StyleGAN to 3D faces have emerged, a corresponding generic 3D GAN inversion
framework is still missing, limiting the applications of 3D face reconstruction
and semantic editing. In this paper, we study the challenging problem of 3D GAN
inversion where a latent code is predicted given a single face image to
faithfully recover its 3D shapes and detailed textures. The problem is
ill-posed: innumerable compositions of shape and texture could be rendered to
the current image. Furthermore, with the limited capacity of a global latent
code, 2D inversion methods cannot preserve faithful shape and texture at the
same time when applied to 3D models. To solve this problem, we devise an
effective self-training scheme to constrain the learning of inversion. The
learning is done efficiently without any real-world 2D-3D training pairs but
proxy samples generated from a 3D GAN. In addition, apart from a global latent
code that captures the coarse shape and texture information, we augment the
generation network with a local branch, where pixel-aligned features are added
to faithfully reconstruct face details. We further consider a new pipeline to
perform 3D view-consistent editing. Extensive experiments show that our method
outperforms state-of-the-art inversion methods in both shape and texture
reconstruction quality. Code and data will be released.
