  Making generative models 3D-aware bridges the 2D image space and the 3D
physical world yet remains challenging. Recent attempts equip a Generative
Adversarial Network (GAN) with a Neural Radiance Field (NeRF), which maps 3D
coordinates to pixel values, as a 3D prior. However, the implicit function in
NeRF has a very local receptive field, making the generator hard to become
aware of the global structure. Meanwhile, NeRF is built on volume rendering
which can be too costly to produce high-resolution results, increasing the
optimization difficulty. To alleviate these two problems, we propose a novel
framework, termed as VolumeGAN, for high-fidelity 3D-aware image synthesis,
through explicitly learning a structural representation and a textural
representation. We first learn a feature volume to represent the underlying
structure, which is then converted to a feature field using a NeRF-like model.
The feature field is further accumulated into a 2D feature map as the textural
representation, followed by a neural renderer for appearance synthesis. Such a
design enables independent control of the shape and the appearance. Extensive
experiments on a wide range of datasets show that our approach achieves
sufficiently higher image quality and better 3D control than the previous
methods.
