  We present a method for learning to generate unbounded flythrough videos of
natural scenes starting from a single view, where this capability is learned
from a collection of single photographs, without requiring camera poses or even
multiple views of each scene. To achieve this, we propose a novel
self-supervised view generation training paradigm, where we sample and
rendering virtual camera trajectories, including cyclic ones, allowing our
model to learn stable view generation from a collection of single views. At
test time, despite never seeing a video during training, our approach can take
a single image and generate long camera trajectories comprised of hundreds of
new views with realistic and diverse content. We compare our approach with
recent state-of-the-art supervised view generation methods that require posed
multi-view videos and demonstrate superior performance and synthesis quality.
