  We present a method, Neural Radiance Flow (NeRFlow),to learn a 4D
spatial-temporal representation of a dynamic scene from a set of RGB images.
Key to our approach is the use of a neural implicit representation that learns
to capture the 3D occupancy, radiance, and dynamics of the scene. By enforcing
consistency across different modalities, our representation enables multi-view
rendering in diverse dynamic scenes, including water pouring, robotic
interaction, and real images, outperforming state-of-the-art methods for
spatial-temporal view synthesis. Our approach works even when inputs images are
captured with only one camera. We further demonstrate that the learned
representation can serve as an implicit scene prior, enabling video processing
tasks such as image super-resolution and de-noising without any additional
supervision.
