Motion Estimation

123 papers with code • 0 benchmarks • 8 datasets

Motion Estimation is used to determine the block-wise or pixel-wise motion vectors between two frames.

Source: MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement


Use these libraries to find Motion Estimation models and implementations

Most implemented papers

Digging Into Self-Supervised Monocular Depth Estimation

nianticlabs/monodepth2 4 Jun 2018

Per-pixel ground-truth depth data is challenging to acquire at scale.

The Double Sphere Camera Model

ethz-asl/kalibr 24 Jul 2018

We evaluate the model using a calibration dataset with several different lenses and compare the models using the metrics that are relevant for Visual Odometry, i. e., reprojection error, as well as computation time for projection and unprojection functions and their Jacobians.

On human motion prediction using recurrent neural networks

una-dinosauria/human-motion-prediction CVPR 2017

Human motion modelling is a classical problem at the intersection of graphics and computer vision, with applications spanning human-computer interaction, motion synthesis, and motion prediction for virtual and augmented reality.

Visual-Inertial Mapping with Non-Linear Factor Recovery

minxuanjun/basalt_class 13 Apr 2019

We reconstruct a set of non-linear factors that make an optimal approximation of the information on the trajectory accumulated by VIO.

Video Enhancement with Task-Oriented Flow

anchen1011/toflow 24 Nov 2017

Many video enhancement algorithms rely on optical flow to register frames in a video sequence.

FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation

m-tassano/fastdvdnet CVPR 2020

In this paper, we propose a state-of-the-art video denoising algorithm based on a convolutional neural network architecture.

DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks

fshamshirdar/DeepVO 25 Sep 2017

This paper presents a novel end-to-end framework for monocular VO by using deep Recurrent Convolutional Neural Networks (RCNNs).

HP-GAN: Probabilistic 3D human motion prediction via GAN

ebarsoum/hpgan 27 Nov 2017

Our model, which we call HP-GAN, learns a probability density function of future human poses conditioned on previous poses.

GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose

yzcjtr/GeoNet CVPR 2018

We propose GeoNet, a jointly unsupervised learning framework for monocular depth, optical flow and ego-motion estimation from videos.