  We introduce the problem of perpetual view generation - long-range generation
of novel views corresponding to an arbitrarily long camera trajectory given a
single image. This is a challenging problem that goes far beyond the
capabilities of current view synthesis methods, which quickly degenerate when
presented with large camera motions. Methods for video generation also have
limited ability to produce long sequences and are often agnostic to scene
geometry. We take a hybrid approach that integrates both geometry and image
synthesis in an iterative `\emph{render}, \emph{refine} and \emph{repeat}'
framework, allowing for long-range generation that cover large distances after
hundreds of frames. Our approach can be trained from a set of monocular video
sequences. We propose a dataset of aerial footage of coastal scenes, and
compare our method with recent view synthesis and conditional video generation
baselines, showing that it can generate plausible scenes for much longer time
horizons over large camera trajectories compared to existing methods. Project
page at https://infinite-nature.github.io/.
