  We present a method to perform novel view and time synthesis of dynamic
scenes, requiring only a monocular video with known camera poses as input. To
do this, we introduce Neural Scene Flow Fields, a new representation that
models the dynamic scene as a time-variant continuous function of appearance,
geometry, and 3D scene motion. Our representation is optimized through a neural
network to fit the observed input views. We show that our representation can be
used for complex dynamic scenes, including thin structures, view-dependent
effects, and natural degrees of motion. We conduct a number of experiments that
demonstrate our approach significantly outperforms recent monocular view
synthesis methods, and show qualitative results of space-time view synthesis on
a variety of real-world videos.
