  We present an algorithm for generating novel views at arbitrary viewpoints
and any input time step given a monocular video of a dynamic scene. Our work
builds upon recent advances in neural implicit representation and uses
continuous and differentiable functions for modeling the time-varying structure
and the appearance of the scene. We jointly train a time-invariant static NeRF
and a time-varying dynamic NeRF, and learn how to blend the results in an
unsupervised manner. However, learning this implicit function from a single
video is highly ill-posed (with infinitely many solutions that match the input
video). To resolve the ambiguity, we introduce regularization losses to
encourage a more physically plausible solution. We show extensive quantitative
and qualitative results of dynamic view synthesis from casually captured
videos.
