SOTAVerified

Optical Flow Estimation

Optical Flow Estimation is a computer vision task that involves computing the motion of objects in an image or a video sequence. The goal of optical flow estimation is to determine the movement of pixels or features in the image, which can be used for various applications such as object tracking, motion analysis, and video compression.

Approaches for optical flow estimation include correlation-based, block-matching, feature tracking, energy-based, and more recently gradient-based.

Further readings:

Definition source: Devon: Deformable Volume Network for Learning Optical Flow

Image credit: Optical Flow Estimation

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 901–950 of 2184 papers

TitleStatusHype
Joint Coarse-And-Fine Reasoning for Deep Optical Flow—0
HashEncoding: Autoencoding with Multiscale Coordinate Hashing—0
Exploiting Semantic Information and Deep Matching for Optical Flow—0
HDRVideo-GAN: Deep Generative HDR Video Reconstruction—0
A spectral optical flow method for determining velocities from digital imagery—0
Exploiting Inter-Frame Regional Correlation for Efficient Action Recognition—0
Exploiting Inductive Biases in Video Modeling through Neural CDEs—0
Convective regularization for optical flow—0
A Framework for Real-Time Face and Facial Feature Tracking using Optical Flow Pre-estimation and Template Tracking—0
Controllable Animation of Fluid Elements in Still Images—0
Exploiting Correspondences with All-pairs Correlations for Multi-view Depth Estimation—0
Hierarchical Attention Learning of Scene Flow in 3D Point Clouds—0
A Spatial-Temporal Dual-Mode Mixed Flow Network for Panoramic Video Salient Object Detection—0
Hierarchical Flow Diffusion for Efficient Frame Interpolation—0
Explicit Spatiotemporal Joint Relation Learning for Tracking Human Pose—0
Hierarchical interaction network for video object segmentation from referring expressions—0
Hierarchically-Constrained Optical Flow—0
Hierarchical Metric Learning and Matching for 2D and 3D Geometric Correspondences—0
Explicit Motion Handling and Interactive Prompting for Video Camouflaged Object Detection—0
3D human pose estimation with adaptive receptive fields and dilated temporal convolutions—0
INV-Flow2PoseNet: Light-Resistant Rigid Object Pose from Optical Flow of RGB-D Images using Images, Normals and Vertices—0
High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks—0
Explicit Depth-Aware Blurry Video Frame Interpolation Guided by Differential Curves—0
Contour Flow: Middle-Level Motion Estimation by Combining Motion Segmentation and Contour Alignment—0
Evolving Losses for Unlabeled Video Representation Learning—0
Continuous Space-Time Video Super-Resolution Utilizing Long-Range Temporal Information—0
Hindsight for Foresight: Unsupervised Structured Dynamics Models from Physical Interaction—0
Demonstration of Vector Flow Imaging using Convolutional Neural Networks—0
HMFlow: Hybrid Matching Optical Flow Network for Small and Fast-Moving Objects—0
How Do Neural Networks Estimate Optical Flow? A Neuropsychology-Inspired Study—0
Evolvement Constrained Adversarial Learning for Video Style Transfer—0
Dense, Accurate Optical Flow Estimation With Piecewise Parametric Model—0
Continual Occlusions and Optical Flow Estimation—0
Human Action Recognition System using Good Features and Multilayer Perceptron Network—0
Human Action Recognition using Local Two-Stream Convolution Neural Network Features and Support Vector Machines—0
Human Following for Wheeled Robot with Monocular Pan-tilt Camera—0
HuPerFlow: A Comprehensive Benchmark for Human vs. Machine Motion Estimation Comparison—0
HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture—0
A Simple Baseline for Pose Tracking in Videos of Crowded Scenes—0
Evolution of Robust High Speed Optical-Flow-Based Landing for Autonomous MAVs—0
Hybrid Learning of Optical Flow and Next Frame Prediction to Boost Optical Flow in the Wild—0
Hybrid Local-Global Context Learning for Neural Video Compression—0
EV-MGRFlowNet: Motion-Guided Recurrent Network for Unsupervised Event-based Optical Flow with Hybrid Motion-Compensation Loss—0
Continual Learning of Conjugated Visual Representations through Higher-order Motion Flows—0
Dense Optical Flow Estimation Using Sparse Regularizers from Reduced Measurements—0
ICANet: A Method of Short Video Emotion Recognition Driven by Multimodal Data—0
A Framework for Learning Invariant Physical Relations in Multimodal Sensory Processing—0
IFOR: Iterative Flow Minimization for Robotic Object Rearrangement—0
Investigation on Combining 3D Convolution of Image Data and Optical Flow to Generate Temporal Action Proposals—0
Joint direct estimation of 3D geometry and 3D motion using spatio temporal gradients—0
Show:102550
← PrevPage 19 of 44Next →
All datasetsSintel-cleanSintel-finalKITTI 2015 (train)KITTI 2015KITTI 2012SpringSintel Clean unsupervisedSintel Final unsupervisedKITTI 2015 unsupervisedKITTI 2012 unsupervised

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SpynetAverage End-Point Error6.64—Unverified
2FastFlowNet-ftAverage End-Point Error4.89—Unverified
3UnrolledCostAverage End-Point Error4.69—Unverified
4LiteFlowNet-ftAverage End-Point Error4.54—Unverified
5FlowNet2Average End-Point Error3.96—Unverified
6IRR-PWCAverage End-Point Error3.84—Unverified
7SelFlowAverage End-Point Error3.74—Unverified
8FDFlowNet-ftAverage End-Point Error3.71—Unverified
9ScopeFlowAverage End-Point Error3.59—Unverified
10LiteFlowNet2-ftAverage End-Point Error3.48—Unverified
#ModelMetricClaimedVerifiedStatus
1SpynetAverage End-Point Error8.36—Unverified
2FastFlowNet-ftAverage End-Point Error6.08—Unverified
3UnrolledCostAverage End-Point Error5.8—Unverified
4LiteFlowNet-ftAverage End-Point Error5.38—Unverified
5MR-FlowAverage End-Point Error5.38—Unverified
6FDFlowNet-ftAverage End-Point Error5.11—Unverified
7LiteFlowNet2-ftAverage End-Point Error4.69—Unverified
8IRR-PWCAverage End-Point Error4.58—Unverified
9LiteFlowNet3-SAverage End-Point Error4.53—Unverified
10ContinualFlow + ftAverage End-Point Error4.52—Unverified
#ModelMetricClaimedVerifiedStatus
1PWC-NetF1-all33.7—Unverified
2FastFlowNetF1-all33.1—Unverified
3FlowNet2F1-all30—Unverified
4VCNF1-all25.1—Unverified
5HD3F1-all24—Unverified
6MaskFlowNetF1-all23.1—Unverified
7SCVF1-all19.3—Unverified
8RAPIDFlowF1-all17.7—Unverified
9CRAFTF1-all17.5—Unverified
10RAFTF1-all17.4—Unverified
#ModelMetricClaimedVerifiedStatus
1FastFlowNet-ftFl-all11.22—Unverified
2UnrolledCostFl-all10.81—Unverified
3LiteFlowNet-ftFl-all9.38—Unverified
4SelFlowFl-all8.42—Unverified
5IRR-PWCFl-all7.65—Unverified
6LiteFlowNet2-ftFl-all7.62—Unverified
7LiteFlowNet3Fl-all7.34—Unverified
8LiteFlowNet3-SFl-all7.22—Unverified
9MaskFlownet-SFl-all6.81—Unverified
10RAPIDFlowFl-all6.12—Unverified
#ModelMetricClaimedVerifiedStatus
1FastFlowNet-ftAverage End-Point Error1.8—Unverified
2IRR-PWCAverage End-Point Error1.6—Unverified
3LiteFlowNet-ftAverage End-Point Error1.6—Unverified
4PWC-Net + ft - axXivAverage End-Point Error1.5—Unverified
5FDFlowNet-ftAverage End-Point Error1.5—Unverified
6SelFlowAverage End-Point Error1.5—Unverified
7LiteFlowNet2-ftAverage End-Point Error1.4—Unverified
8LiteFlowNet3Average End-Point Error1.3—Unverified
9LiteFlowNet3-SAverage End-Point Error1.3—Unverified
10MaskFlownet-SAverage End-Point Error1.1—Unverified
#ModelMetricClaimedVerifiedStatus
1PWCNet1px total82.27—Unverified
2SPyNet1px total29.96—Unverified
3GMFlow1px total10.36—Unverified
4GMA1px total7.07—Unverified
5RAFT1px total6.79—Unverified
6FlowNet21px total6.71—Unverified
7FlowFormer1px total6.51—Unverified
8MS-RAFT+1px total5.72—Unverified
9RPKNet1px total4.81—Unverified
10DPFlow1px total3.44—Unverified
#ModelMetricClaimedVerifiedStatus
1UFlowAverage End-Point Error5.21—Unverified
2MDFlow-FastAverage End-Point Error4.73—Unverified
3UpFlowAverage End-Point Error4.68—Unverified
4ARFlow-MVAverage End-Point Error4.49—Unverified
5MDFlowAverage End-Point Error4.16—Unverified
#ModelMetricClaimedVerifiedStatus
1UFlowAverage End-Point Error6.5—Unverified
2MDFlow-FastAverage End-Point Error5.99—Unverified
3ARFlow-MVAverage End-Point Error5.67—Unverified
4MDFlowAverage End-Point Error5.46—Unverified
5UpFlowAverage End-Point Error5.32—Unverified
#ModelMetricClaimedVerifiedStatus
1ARFlow-MVFl-all11.79—Unverified
2MDFlow-FastFl-all11.43—Unverified
3UpFlowFl-all9.38—Unverified
4MDFlowFl-all8.91—Unverified
#ModelMetricClaimedVerifiedStatus
1ARFlow-MVAverage End-Point Error1.5—Unverified
2UpFlowAverage End-Point Error1.4—Unverified