  Neural Radiance Fields (NeRFs) have emerged as a powerful neural 3D
representation for objects and scenes derived from 2D data. Generating NeRFs,
however, remains difficult in many scenarios. For instance, training a NeRF
with only a small number of views as supervision remains challenging since it
is an under-constrained problem. In such settings, it calls for some inductive
prior to filter out bad local minima. One way to introduce such inductive
priors is to learn a generative model for NeRFs modeling a certain class of
scenes. In this paper, we propose to use a diffusion model to generate NeRFs
encoded on a regularized grid. We show that our model can sample realistic
NeRFs, while at the same time allowing conditional generations, given a certain
observation as guidance.
