  We consider the problem of reconstructing a full 360{\deg} photographic model
of an object from a single image of it. We do so by fitting a neural radiance
field to the image, but find this problem to be severely ill-posed. We thus
take an off-the-self conditional image generator based on diffusion and
engineer a prompt that encourages it to "dream up" novel views of the object.
Using an approach inspired by DreamFields and DreamFusion, we fuse the given
input view, the conditional prior, and other regularizers in a final,
consistent reconstruction. We demonstrate state-of-the-art reconstruction
results on benchmark images when compared to prior methods for monocular 3D
reconstruction of objects. Qualitatively, our reconstructions provide a
faithful match of the input view and a plausible extrapolation of its
appearance and 3D shape, including to the side of the object not visible in the
image.
