  We present a high-fidelity 3D generative adversarial network (GAN) inversion
framework that can synthesize photo-realistic novel views while preserving
specific details of the input image. High-fidelity 3D GAN inversion is
inherently challenging due to the geometry-texture trade-off in 3D inversion,
where overfitting to a single view input image often damages the estimated
geometry during the latent optimization. To solve this challenge, we propose a
novel pipeline that builds on the pseudo-multi-view estimation with visibility
analysis. We keep the original textures for the visible parts and utilize
generative priors for the occluded parts. Extensive experiments show that our
approach achieves advantageous reconstruction and novel view synthesis quality
over state-of-the-art methods, even for images with out-of-distribution
textures. The proposed pipeline also enables image attribute editing with the
inverted latent code and 3D-aware texture modification. Our approach enables
high-fidelity 3D rendering from a single image, which is promising for various
applications of AI-generated 3D content.
