  The advent of generative radiance fields has significantly promoted the
development of 3D-aware image synthesis. The cumulative rendering process in
radiance fields makes training these generative models much easier since
gradients are distributed over the entire volume, but leads to diffused object
surfaces. In the meantime, compared to radiance fields occupancy
representations could inherently ensure deterministic surfaces. However, if we
directly apply occupancy representations to generative models, during training
they will only receive sparse gradients located on object surfaces and
eventually suffer from the convergence problem. In this paper, we propose
Generative Occupancy Fields (GOF), a novel model based on generative radiance
fields that can learn compact object surfaces without impeding its training
convergence. The key insight of GOF is a dedicated transition from the
cumulative rendering in radiance fields to rendering with only the surface
points as the learned surface gets more and more accurate. In this way, GOF
combines the merits of two representations in a unified framework. In practice,
the training-time transition of start from radiance fields and march to
occupancy representations is achieved in GOF by gradually shrinking the
sampling region in its rendering process from the entire volume to a minimal
neighboring region around the surface. Through comprehensive experiments on
multiple datasets, we demonstrate that GOF can synthesize high-quality images
with 3D consistency and simultaneously learn compact and smooth object
surfaces. Code, models, and demo videos are available at
https://sheldontsui.github.io/projects/GOF
