Quadratic Video Interpolation
Xiangyu Xu, Li Siyao, Wenxiu Sun, Qian Yin, Ming-Hsuan Yang
Abstract
Video interpolation is an important problem in computer vision, which helps overcome the temporal limitation of camera sensors. Existing video interpolation methods usually assume uniform motion between consecutive frames and use linear models for interpolation, which cannot well approximate the complex motion in the real world. To address these issues, we propose a quadratic video interpolation method which exploits the acceleration information in videos. This method allows prediction with curvilinear trajectory and variable velocity, and generates more accurate interpolation results. For high-quality frame synthesis, we develop a flow reversal layer to estimate flow fields starting from the unknown target frame to the source frame. In addition, we present techniques for flow refinement. Extensive experiments demonstrate that our approach performs favorably against the existing linear models on a wide variety of video datasets.
BibTeX
@inproceedings{NEURIPS2019_d045c59a,
author = {Xu, Xiangyu and Siyao, Li and Sun, Wenxiu and Yin, Qian and Yang, Ming-Hsuan},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Quadratic Video Interpolation},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/d045c59a90d7587d8d671b5f5aec4e7c-Paper.pdf},
volume = {32},
year = {2019}
}