  Tremendous progress in deep generative models has led to photorealistic image
synthesis. While achieving compelling results, most approaches operate in the
two-dimensional image domain, ignoring the three-dimensional nature of our
world. Several recent works therefore propose generative models which are
3D-aware, i.e., scenes are modeled in 3D and then rendered differentiably to
the image plane. This leads to impressive 3D consistency, but incorporating
such a bias comes at a price: the camera needs to be modeled as well. Current
approaches assume fixed intrinsics and a predefined prior over camera pose
ranges. As a result, parameter tuning is typically required for real-world
data, and results degrade if the data distribution is not matched. Our key
hypothesis is that learning a camera generator jointly with the image generator
leads to a more principled approach to 3D-aware image synthesis. Further, we
propose to decompose the scene into a background and foreground model, leading
to more efficient and disentangled scene representations. While training from
raw, unposed image collections, we learn a 3D- and camera-aware generative
model which faithfully recovers not only the image but also the camera data
distribution. At test time, our model generates images with explicit control
over the camera as well as the shape and appearance of the scene.
