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 …

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

Cell phone cameras have small apertures, which limits the number of photons they can
gather, leading to noisy images in low light. They also have small sensor pixels, which limits …

It has been recently shown that a convolutional neural network can learn optical flow
estimation with unsuper-vised learning. However, the performance of the unsuper-vised …

Most recent works in optical flow extraction focus on the accuracy and neglect the time
complexity. However, in real-life visual applications, such as tracking, activity detection and …

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 …

Standard approaches to computing interpolated (in-between) frames in a video sequence
require accurate pixel correspondences between images eg using optical flow. We present …

We address the elusive goal of estimating optical flow both accurately and efficiently by
adopting a sparse-to-dense approach. Given a set of sparse matches, we regress to dense …

Given a stereo pair it is possible to recover a depth map and use that depth to render a
synthetically defocused image. Though stereo algorithms are well-studied, rarely are those …