  Novel view synthesis (NVS) and video prediction (VP) are typically considered
disjoint tasks in computer vision. However, they can both be seen as ways to
observe the spatial-temporal world: NVS aims to synthesize a scene from a new
point of view, while VP aims to see a scene from a new point of time. These two
tasks provide complementary signals to obtain a scene representation, as
viewpoint changes from spatial observations inform depth, and temporal
observations inform the motion of cameras and individual objects. Inspired by
these observations, we propose to study the problem of Video Extrapolation in
Space and Time (VEST). We propose a model that leverages the self-supervision
and the complementary cues from both tasks, while existing methods can only
solve one of them. Experiments show that our method achieves performance better
than or comparable to several state-of-the-art NVS and VP methods on indoor and
outdoor real-world datasets.
