  We present a method for estimating neural scenes representations of objects
given only a single image. The core of our method is the estimation of a
geometric scaffold for the object and its use as a guide for the reconstruction
of the underlying radiance field. Our formulation is based on a generative
process that first maps a latent code to a voxelized shape, and then renders it
to an image, with the object appearance being controlled by a second latent
code. During inference, we optimize both the latent codes and the networks to
fit a test image of a new object. The explicit disentanglement of shape and
appearance allows our model to be fine-tuned given a single image. We can then
render new views in a geometrically consistent manner and they represent
faithfully the input object. Additionally, our method is able to generalize to
images outside of the training domain (more realistic renderings and even real
photographs). Finally, the inferred geometric scaffold is itself an accurate
estimate of the object's 3D shape. We demonstrate in several experiments the
effectiveness of our approach in both synthetic and real images.
