Video Frame Interpolation (VFI) is a fascinating and challenging problem in the computer
vision (CV) field, aiming to generate non-existing frames between two consecutive video …

Many video enhancement algorithms rely on optical flow to register frames in a video
sequence. Precise flow estimation is however intractable; and optical flow itself is often a …

Prevailing video frame interpolation techniques rely heavily on optical flow estimation and
require additional model complexity and computational cost; it is also susceptible to error …

Standard video frame interpolation methods first estimate optical flow between input frames
and then synthesize an intermediate frame guided by motion. Recent approaches merge …

Video frame interpolation algorithms typically estimate optical flow or its variations and then
use it to guide the synthesis of an intermediate frame between two consecutive original …

Video frame interpolation typically involves two steps: motion estimation and pixel synthesis.
Such a two-step approach heavily depends on the quality of motion estimation. This paper …

Video frame interpolation is one of the most challenging tasks in video processing research.
Recently, many studies based on deep learning have been suggested. Most of these …

Optical flow estimation is one of the oldest and still most active research domains in
computer vision. In 35 years, many methodological concepts have been introduced and …

DNN-based frame interpolation--that generates the intermediate frames given two
consecutive frames--typically relies on heavy model architectures with a huge number of …

Video frame interpolation is an important area in the computer vision research activities for
video post-processing, surveillance, and video restoration tasks. It aims toward increasing …