  While 2D generative adversarial networks have enabled high-resolution image
synthesis, they largely lack an understanding of the 3D world and the image
formation process. Thus, they do not provide precise control over camera
viewpoint or object pose. To address this problem, several recent approaches
leverage intermediate voxel-based representations in combination with
differentiable rendering. However, existing methods either produce low image
resolution or fall short in disentangling camera and scene properties, e.g.,
the object identity may vary with the viewpoint. In this paper, we propose a
generative model for radiance fields which have recently proven successful for
novel view synthesis of a single scene. In contrast to voxel-based
representations, radiance fields are not confined to a coarse discretization of
the 3D space, yet allow for disentangling camera and scene properties while
degrading gracefully in the presence of reconstruction ambiguity. By
introducing a multi-scale patch-based discriminator, we demonstrate synthesis
of high-resolution images while training our model from unposed 2D images
alone. We systematically analyze our approach on several challenging synthetic
and real-world datasets. Our experiments reveal that radiance fields are a
powerful representation for generative image synthesis, leading to 3D
consistent models that render with high fidelity.
