CURE: Learning Cross-Video Neural Representations
for High-Quality Frame Interpolation

The FIRST Neural Field (NF) based Video Frame Interpolation (VFI) Algorithm.


Wentao Shangguan*, Yu Sun*, Weijie Gan, Ulugbek S. Kamilov
Computational Imaging Group (CIG)

Paper Video Code

Abstract


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 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. 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 to learn a prior across multiple videos. Experimental evaluations show that achieves the state-of-the-art performance on video interpolation on several benchmark datasets.

Model


Video Interpolation


Input Video

CURE

Try Yourself


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Static Comparasions with SOTA


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Continuous time interpolation, Car @X4K

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Playground, t=0.5 @Nvidia Dynamic Scene

Fig1

Airplane, t=0.5 @Vimeo90K

Fig2

Bird, t=0.5 @SNU-FILM

Paper


Bibtex


@article{shangguan2022cure, title={Learning Cross-Video Neural Representations for High-Quality Frame Interpolation}, author={Wentao Shangguan and Yu Sun and Weijie Gan and Ulugbek S. Kamilov}, year={2022}, note={arXiv:2203.00137} }