Pseudo-Generalized Dynamic View Synthesis from a Video
This addresses the challenge of dynamic novel view synthesis for computer vision applications, offering a generalized approach that is incremental but advances the field.
The paper tackles the problem of rendering dynamic scenes from novel viewpoints using only a monocular video, proposing a pseudo-generalized method that avoids scene-specific appearance optimization and improves upon some scene-specific techniques.
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.