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.

提出了一种高保真的三维对抗生成网络，可以在保留输入图像的特定细节的同时合成逼真的新视图。该方法通过伪多视图估计和能见度分析建立，有效解决了三维反演中的几何纹理平衡问题，并成功实现了单张图像的高保真三维重建和新视图合成。并且本方法亦适用于图像属性编辑和 3D 纹理修改，因此有望被广泛应用于人工智能生成的三维内容领域。