  Existing 3D-aware facial generation methods face a dilemma in quality versus
editability: they either generate editable results in low resolution or
high-quality ones with no editing flexibility. In this work, we propose a new
approach that brings the best of both worlds together. Our system consists of
three major components: (1) a 3D-semantics-aware generative model that produces
view-consistent, disentangled face images and semantic masks; (2) a hybrid GAN
inversion approach that initialize the latent codes from the semantic and
texture encoder, and further optimized them for faithful reconstruction; and
(3) a canonical editor that enables efficient manipulation of semantic masks in
canonical view and product high-quality editing results. Our approach is
competent for many applications, e.g. free-view face drawing, editing, and
style control. Both quantitative and qualitative results show that our method
reaches the state-of-the-art in terms of photorealism, faithfulness, and
efficiency.
