  We propose NeRF-VAE, a 3D scene generative model that incorporates geometric
structure via NeRF and differentiable volume rendering. In contrast to NeRF,
our model takes into account shared structure across scenes, and is able to
infer the structure of a novel scene -- without the need to re-train -- using
amortized inference. NeRF-VAE's explicit 3D rendering process further contrasts
previous generative models with convolution-based rendering which lacks
geometric structure. Our model is a VAE that learns a distribution over
radiance fields by conditioning them on a latent scene representation. We show
that, once trained, NeRF-VAE is able to infer and render
geometrically-consistent scenes from previously unseen 3D environments using
very few input images. We further demonstrate that NeRF-VAE generalizes well to
out-of-distribution cameras, while convolutional models do not. Finally, we
introduce and study an attention-based conditioning mechanism of NeRF-VAE's
decoder, which improves model performance.
