Unsupervised generation of high-quality multi-view-consistent images and 3D
shapes using only collections of single-view 2D photographs has been a
long-standing challenge. Existing 3D GANs are either compute-intensive or make
approximations that are not 3D-consistent; the former limits quality and
resolution of the generated images and the latter adversely affects multi-view
consistency and shape quality. In this work, we improve the computational
efficiency and image quality of 3D GANs without overly relying on these
approximations. We introduce an expressive hybrid explicit-implicit network
architecture that, together with other design choices, synthesizes not only
high-resolution multi-view-consistent images in real time but also produces
high-quality 3D geometry. By decoupling feature generation and neural
rendering, our framework is able to leverage state-of-the-art 2D CNN
generators, such as StyleGAN2, and inherit their efficiency and expressiveness.
We demonstrate state-of-the-art 3D-aware synthesis with FFHQ and AFHQ Cats,
among other experiments.

使用单视角 2D 照片集合进行高质量多视角一致的图片和 3D 形状的非监督生成一直是一个难题，本文提出了一种混合显式 - 隐式网络架构，用于提高 3D GANs 的计算效率和图像质量，实现了高分辨率实时多视角一致的图像合成，同时产生高质量的 3D 几何体。