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3D-aware Generative Adversarial Networks (GANs) have shown remarkableprogress in learning to generate multi-view-consistent images and 3D geometriesof scenes from collections of 2D images via neural volume rendering. Yet, thesignificant memory and computational costs of dense sampling in volumerendering have forced 3D GANs to adopt patch-based training or employlow-resolution rendering with post-processing 2D super resolution, whichsacrifices multiview consistency and the quality of resolved geometry.Consequently, 3D GANs have not yet been able to fully resolve the rich 3Dgeometry present in 2D images. In this work, we propose techniques to scaleneural volume rendering to the much higher resolution of native 2D images,thereby resolving fine-grained 3D geometry with unprecedented detail. Ourapproach employs learning-based samplers for accelerating neural rendering for3D GAN training using up to 5 times fewer depth samples. This enables us toexplicitly "render every pixel" of the full-resolution image during trainingand inference without post-processing superresolution in 2D. Together with ourstrategy to learn high-quality surface geometry, our method synthesizeshigh-resolution 3D geometry and strictly view-consistent images whilemaintaining image quality on par with baselines relying on post-processingsuper resolution. We demonstrate state-of-the-art 3D gemetric quality on FFHQand AFHQ, setting a new standard for unsupervised learning of 3D shapes in 3DGANs.

What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs