  Videos typically record the streaming and continuous visual data as discrete
consecutive frames. Since the storage cost is expensive for videos of high
fidelity, most of them are stored in a relatively low resolution and frame
rate. Recent works of Space-Time Video Super-Resolution (STVSR) are developed
to incorporate temporal interpolation and spatial super-resolution in a unified
framework. However, most of them only support a fixed up-sampling scale, which
limits their flexibility and applications. In this work, instead of following
the discrete representations, we propose Video Implicit Neural Representation
(VideoINR), and we show its applications for STVSR. The learned implicit neural
representation can be decoded to videos of arbitrary spatial resolution and
frame rate. We show that VideoINR achieves competitive performances with
state-of-the-art STVSR methods on common up-sampling scales and significantly
outperforms prior works on continuous and out-of-training-distribution scales.
Our project page is at http://zeyuan-chen.com/VideoINR/ .
