  Deep generative models allow for photorealistic image synthesis at high
resolutions. But for many applications, this is not enough: content creation
also needs to be controllable. While several recent works investigate how to
disentangle underlying factors of variation in the data, most of them operate
in 2D and hence ignore that our world is three-dimensional. Further, only few
works consider the compositional nature of scenes. Our key hypothesis is that
incorporating a compositional 3D scene representation into the generative model
leads to more controllable image synthesis. Representing scenes as
compositional generative neural feature fields allows us to disentangle one or
multiple objects from the background as well as individual objects' shapes and
appearances while learning from unstructured and unposed image collections
without any additional supervision. Combining this scene representation with a
neural rendering pipeline yields a fast and realistic image synthesis model. As
evidenced by our experiments, our model is able to disentangle individual
objects and allows for translating and rotating them in the scene as well as
changing the camera pose.
