  Recently, a surge of high-quality 3D-aware GANs have been proposed, which
leverage the generative power of neural rendering. It is natural to associate
3D GANs with GAN inversion methods to project a real image into the generator's
latent space, allowing free-view consistent synthesis and editing, referred as
3D GAN inversion. Although with the facial prior preserved in pre-trained 3D
GANs, reconstructing a 3D portrait with only one monocular image is still an
ill-pose problem. The straightforward application of 2D GAN inversion methods
focuses on texture similarity only while ignoring the correctness of 3D
geometry shapes. It may raise geometry collapse effects, especially when
reconstructing a side face under an extreme pose. Besides, the synthetic
results in novel views are prone to be blurry. In this work, we propose a novel
method to promote 3D GAN inversion by introducing facial symmetry prior. We
design a pipeline and constraints to make full use of the pseudo auxiliary view
obtained via image flipping, which helps obtain a robust and reasonable
geometry shape during the inversion process. To enhance texture fidelity in
unobserved viewpoints, pseudo labels from depth-guided 3D warping can provide
extra supervision. We design constraints aimed at filtering out conflict areas
for optimization in asymmetric situations. Comprehensive quantitative and
qualitative evaluations on image reconstruction and editing demonstrate the
superiority of our method.
