  This paper considers the problem of temporal video interpolation, where the
goal is to synthesize a new video frame given its two neighbors. We propose
Cross-Video Neural Representation (CURE) as the first video interpolation
method based on neural fields (NF). NF refers to the recent class of methods
for the neural representation of complex 3D scenes that has seen widespread
success and application across computer vision. CURE represents the video as a
continuous function parameterized by a coordinate-based neural network, whose
inputs are the spatiotemporal coordinates and outputs are the corresponding RGB
values. CURE introduces a new architecture that conditions the neural network
on the input frames for imposing space-time consistency in the synthesized
video. This not only improves the final interpolation quality, but also enables
CURE to learn a prior across multiple videos. Experimental evaluations show
that CURE achieves the state-of-the-art performance on video interpolation on
several benchmark datasets.
