  We introduce 3inGAN, an unconditional 3D generative model trained from 2D
images of a single self-similar 3D scene. Such a model can be used to produce
3D "remixes" of a given scene, by mapping spatial latent codes into a 3D
volumetric representation, which can subsequently be rendered from arbitrary
views using physically based volume rendering. By construction, the generated
scenes remain view-consistent across arbitrary camera configurations, without
any flickering or spatio-temporal artifacts. During training, we employ a
combination of 2D, obtained through differentiable volume tracing, and 3D
Generative Adversarial Network (GAN) losses, across multiple scales, enforcing
realism on both its 3D structure and the 2D renderings. We show results on
semi-stochastic scenes of varying scale and complexity, obtained from real and
synthetic sources. We demonstrate, for the first time, the feasibility of
learning plausible view-consistent 3D scene variations from a single exemplar
scene and provide qualitative and quantitative comparisons against recent
related methods.
