Video super-resolution (VSR) is reconstructing high-resolution videos from low resolution
ones. Recently, the VSR methods based on deep neural networks have made great …

This paper is intended to pave the way for new researchers in the field of robotics and
autonomous systems, particularly those who are interested in robot localization and …

We present a unified formulation and model for three motion and 3D perception tasks:
optical flow, rectified stereo matching and unrectified stereo depth estimation from posed …

Denoising diffusion probabilistic models have transformed image generation with their
impressive fidelity and diversity. We show that they also excel in estimating optical flow and …

Video super-resolution (VSR) aims to restore a photo-realistic high-resolution (HR) video
frame from both its corresponding low-resolution (LR) frame (reference frame) and multiple …

We present a compact but effective CNN model for optical flow, called PWC-Net. PWC-Net
has been designed according to simple and well-established principles: pyramidal …

Given two consecutive frames, video interpolation aims at generating intermediate frame (s)
to form both spatially and temporally coherent video sequences. While most existing …

We present a self-supervised learning approach for optical flow. Our method distills reliable
flow estimations from non-occluded pixels, and uses these predictions as ground truth to …

Optical flow estimation is a fundamental task in computer vision. Recent direct-regression
methods using deep neural networks achieve remarkable performance improvement …

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