  We study the 3D-aware image attribute editing problem in this paper, which
has wide applications in practice. Recent methods solved the problem by
training a shared encoder to map images into a 3D generator's latent space or
by per-image latent code optimization and then edited images in the latent
space. Despite their promising results near the input view, they still suffer
from the 3D inconsistency of produced images at large camera poses and
imprecise image attribute editing, like affecting unspecified attributes during
editing. For more efficient image inversion, we train a shared encoder for all
images. To alleviate 3D inconsistency at large camera poses, we propose two
novel methods, an alternating training scheme and a multi-view identity loss,
to maintain 3D consistency and subject identity. As for imprecise image
editing, we attribute the problem to the gap between the latent space of real
images and that of generated images. We compare the latent space and inversion
manifold of GAN models and demonstrate that editing in the inversion manifold
can achieve better results in both quantitative and qualitative evaluations.
Extensive experiments show that our method produces more 3D consistent images
and achieves more precise image editing than previous work. Source code and
pretrained models can be found on our project page:
https://mybabyyh.github.io/Preim3D/
