  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.
