Bidirectional Flow Fields for Sparse Input Novel View Synthesis of Dynamic Scenes

Novel view synthesis involves generating unseen perspectives of a scene based on videos captured from limited viewpoints. Learning scene representations of such videos containing dynamic scene elements introduces several challenges in modeling the motion. Existing models typically require dense viewpoint coverage to produce high-quality renderings, and their performance degrades significantly when the number of viewpoints is reduced. In this work, we explore the challenges associated with volumetric motion modeling for synthesizing novel views of dynamic scenes from only a few fixed viewpoints. Specifically, we address the limitations of unidirectional motion models, which often result in a many-to-one mapping of scene points. We enforce cyclic flow consistency with the help of bidirectional motion fields to achieve superior reconstruction of novel views of dynamic scenes. Moreover, the bi-directional motion field design allows us to track object motion in the synthesized views.