  Previous portrait image generation methods roughly fall into two categories:
2D GANs and 3D-aware GANs. 2D GANs can generate high fidelity portraits but
with low view consistency. 3D-aware GAN methods can maintain view consistency
but their generated images are not locally editable. To overcome these
limitations, we propose FENeRF, a 3D-aware generator that can produce
view-consistent and locally-editable portrait images. Our method uses two
decoupled latent codes to generate corresponding facial semantics and texture
in a spatial aligned 3D volume with shared geometry. Benefiting from such
underlying 3D representation, FENeRF can jointly render the boundary-aligned
image and semantic mask and use the semantic mask to edit the 3D volume via GAN
inversion. We further show such 3D representation can be learned from widely
available monocular image and semantic mask pairs. Moreover, we reveal that
joint learning semantics and texture helps to generate finer geometry. Our
experiments demonstrate that FENeRF outperforms state-of-the-art methods in
various face editing tasks.
