ZIGNeRF: Zero-shot 3D Scene Representation with Invertible Generative
  Neural Radiance Fields
Kanghyeok Ko Minhyeok Lee
Generative Neural Radiance Fields (NeRFs) have demonstrated remarkable
proficiency in synthesizing multi-view images by learning the distribution of a
set of unposed images. Despite the aptitude of existing generative NeRFs in
generating 3D-consistent high-quality random samples within data distribution,
the creation of a 3D representation of a singular input image remains a
formidable challenge. In this manuscript, we introduce ZIGNeRF, an innovative
model that executes zero-shot Generative Adversarial Network (GAN) inversion
for the generation of multi-view images from a single out-of-domain image. The
model is underpinned by a novel inverter that maps out-of-domain images into
the latent code of the generator manifold. Notably, ZIGNeRF is capable of
disentangling the object from the background and executing 3D operations such
as 360-degree rotation or depth and horizontal translation. The efficacy of our
model is validated using multiple real-image datasets: Cats, AFHQ, CelebA,
CelebA-HQ, and CompCars.