  Rendering scenes observed in a monocular video from novel viewpoints is a
challenging problem. For static scenes the community has studied both
scene-specific optimization techniques, which optimize on every test scene, and
generalized techniques, which only run a deep net forward pass on a test scene.
In contrast, for dynamic scenes, scene-specific optimization techniques exist,
but, to our best knowledge, there is currently no generalized method for
dynamic novel view synthesis from a given monocular video. To answer whether
generalized dynamic novel view synthesis from monocular videos is possible
today, we establish an analysis framework based on existing techniques and work
toward the generalized approach. We find a pseudo-generalized process without
scene-specific appearance optimization is possible, but geometrically and
temporally consistent depth estimates are needed. Despite no scene-specific
appearance optimization, the pseudo-generalized approach improves upon some
scene-specific methods.
