  We present a 3D generative model for general natural scenes. Lacking
necessary volumes of 3D data characterizing the target scene, we propose to
learn from a single scene. Our key insight is that a natural scene often
contains multiple constituents whose geometry, texture, and spatial
arrangements follow some clear patterns, but still exhibit rich variations over
different regions within the same scene. This suggests localizing the learning
of a generative model on substantial local regions. Hence, we exploit a
multi-scale convolutional network, which possesses the spatial locality bias in
nature, to learn from the statistics of local regions at multiple scales within
a single scene. In contrast to existing methods, our learning setup bypasses
the need to collect data from many homogeneous 3D scenes for learning common
features. We coin our method SinGRAV, for learning a Generative RAdiance Volume
from a Single natural scene. We demonstrate the ability of SinGRAV in
generating plausible and diverse variations from a single scene, the merits of
SinGRAV over state-of-the-art generative neural scene methods, as well as the
versatility of SinGRAV by its use in a variety of applications, spanning 3D
scene editing, composition, and animation. Code and data will be released to
facilitate further research.
