  We propose im2nerf, a learning framework that predicts a continuous neural
object representation given a single input image in the wild, supervised by
only segmentation output from off-the-shelf recognition methods. The standard
approach to constructing neural radiance fields takes advantage of multi-view
consistency and requires many calibrated views of a scene, a requirement that
cannot be satisfied when learning on large-scale image data in the wild. We
take a step towards addressing this shortcoming by introducing a model that
encodes the input image into a disentangled object representation that contains
a code for object shape, a code for object appearance, and an estimated camera
pose from which the object image is captured. Our model conditions a NeRF on
the predicted object representation and uses volume rendering to generate
images from novel views. We train the model end-to-end on a large collection of
input images. As the model is only provided with single-view images, the
problem is highly under-constrained. Therefore, in addition to using a
reconstruction loss on the synthesized input view, we use an auxiliary
adversarial loss on the novel rendered views. Furthermore, we leverage object
symmetry and cycle camera pose consistency. We conduct extensive quantitative
and qualitative experiments on the ShapeNet dataset as well as qualitative
experiments on Open Images dataset. We show that in all cases, im2nerf achieves
the state-of-the-art performance for novel view synthesis from a single-view
unposed image in the wild.
