  Recent works have shown that 3D-aware GANs trained on unstructured single
image collections can generate multiview images of novel instances. The key
underpinnings to achieve this are a 3D radiance field generator and a volume
rendering process. However, existing methods either cannot generate
high-resolution images (e.g., up to 256X256) due to the high computation cost
of neural volume rendering, or rely on 2D CNNs for image-space upsampling which
jeopardizes the 3D consistency across different views. This paper proposes a
novel 3D-aware GAN that can generate high resolution images (up to 1024X1024)
while keeping strict 3D consistency as in volume rendering. Our motivation is
to achieve super-resolution directly in the 3D space to preserve 3D
consistency. We avoid the otherwise prohibitively-expensive computation cost by
applying 2D convolutions on a set of 2D radiance manifolds defined in the
recent generative radiance manifold (GRAM) approach, and apply dedicated loss
functions for effective GAN training at high resolution. Experiments on FFHQ
and AFHQv2 datasets show that our method can produce high-quality 3D-consistent
results that significantly outperform existing methods. It makes a significant
step towards closing the gap between traditional 2D image generation and
3D-consistent free-view generation.
