  Recent approaches to render photorealistic views from a limited set of
photographs have pushed the boundaries of our interactions with pictures of
static scenes. The ability to recreate moments, that is, time-varying
sequences, is perhaps an even more interesting scenario, but it remains largely
unsolved. We introduce DCT-NeRF, a coordinatebased neural representation for
dynamic scenes. DCTNeRF learns smooth and stable trajectories over the input
sequence for each point in space. This allows us to enforce consistency between
any two frames in the sequence, which results in high quality reconstruction,
particularly in dynamic regions.
