  In this paper, we address the space-time video super-resolution, which aims
at generating a high-resolution (HR) slow-motion video from a low-resolution
(LR) and low frame rate (LFR) video sequence. A na\"ive method is to decompose
it into two sub-tasks: video frame interpolation (VFI) and video
super-resolution (VSR). Nevertheless, temporal interpolation and spatial
upscaling are intra-related in this problem. Two-stage approaches cannot fully
make use of this natural property. Besides, state-of-the-art VFI or VSR deep
networks usually have a large frame reconstruction module in order to obtain
high-quality photo-realistic video frames, which makes the two-stage approaches
have large models and thus be relatively time-consuming. To overcome the
issues, we present a one-stage space-time video super-resolution framework,
which can directly reconstruct an HR slow-motion video sequence from an input
LR and LFR video. Instead of reconstructing missing LR intermediate frames as
VFI models do, we temporally interpolate LR frame features of the missing LR
frames capturing local temporal contexts by a feature temporal interpolation
module. Extensive experiments on widely used benchmarks demonstrate that the
proposed framework not only achieves better qualitative and quantitative
performance on both clean and noisy LR frames but also is several times faster
than recent state-of-the-art two-stage networks. The source code is released in
https://github.com/Mukosame/Zooming-Slow-Mo-CVPR-2020 .
