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

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Showing 701–750 of 2184 papers

TitleStatusHype
A Physical Coherence Benchmark for Evaluating Video Generation Models via Optical Flow-guided Frame PredictionCode0
Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow EstimationCode0
Learning Multi-Human Optical FlowCode0
Kinematics Modeling Network for Video-based Human Pose EstimationCode0
Ego-motion Estimation Based on Fusion of Images and EventsCode0
Learning Human Optical FlowCode0
Learning Dynamic Point Cloud Compression via Hierarchical Inter-frame Block MatchingCode0
Deep Motion Blind Video StabilizationCode0
Learning End-To-End Scene Flow by Distilling Single Tasks KnowledgeCode0
FALDOI: A new minimization strategy for large displacement variational optical flowCode0
Learning Blind Video Temporal ConsistencyCode0
Learning Correspondence from the Cycle-Consistency of TimeCode0
Learning Energy Based Inpainting for Optical FlowCode0
Learning on the Edge: Explicit Boundary Handling in CNNsCode0
CloudCast: A Satellite-Based Dataset and Baseline for Forecasting CloudsCode0
LAPNet: Non-rigid Registration derived in k-space for Magnetic Resonance ImagingCode0
Lagrangian Motion Magnification with Double Sparse Optical Flow DecompositionCode0
A Numerical Framework for Efficient Motion Estimation on Evolving Sphere-Like Surfaces based on Brightness and Mass Conservation LawsCode0
Kernel learning for visual perceptionCode0
KORSAL: Key-point Detection based Online Real-Time Spatio-Temporal Action LocalizationCode0
Learnable Cost Volume Using the Cayley RepresentationCode0
Iterative Residual Refinement for Joint Optical Flow and Occlusion EstimationCode0
InterpoNet, A brain inspired neural network for optical flow dense interpolationCode0
A torus model for optical flowCode0
Fast, Robust, Continuous Monocular Egomotion ComputationCode0
Cross Modal Distillation for Supervision TransferCode0
Joint Detection of Motion Boundaries and OcclusionsCode0
Efficient Coarse-To-Fine PatchMatch for Large Displacement Optical FlowCode0
Predicting Deeper into the Future of Semantic SegmentationCode0
Predicting Future Instance Segmentation by Forecasting Convolutional FeaturesCode0
Effective Video Mirror Detection with Inconsistent Motion CuesCode0
Learning Optical Expansion From Scale MatchingCode0
Image Velocimetry using Direct Displacement Field estimation with Neural Networks for FluidsCode0
EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN ScenesCode0
Improving Audio-Visual Segmentation with Bidirectional GenerationCode0
Im2Flow: Motion Hallucination from Static Images for Action RecognitionCode0
eCARLA-scenes: A synthetically generated dataset for event-based optical flow predictionCode0
IDO-VFI: Identifying Dynamics via Optical Flow Guidance for Video Frame Interpolation with EventsCode0
HSTR-Net: High Spatio-Temporal Resolution Video Generation For Wide Area SurveillanceCode0
Features Understanding in 3D CNNs for Actions Recognition in VideoCode0
D3D: Distilled 3D Networks for Video Action RecognitionCode0
An Optical Flow-Based Approach for Minimally-Divergent Velocimetry Data InterpolationCode0
Dynamic Filter NetworksCode0
HMAFlow: Learning More Accurate Optical Flow via Hierarchical Motion Field AlignmentCode0
IBVC: Interpolation-driven B-frame Video CompressionCode0
Inference-Time Gaze Refinement for Micro-Expression Recognition: Enhancing Event-Based Eye Tracking with Motion-Aware Post-ProcessingCode0
Hierarchical Graph Pattern Understanding for Zero-Shot VOSCode0
Hierarchical Discrete Distribution Decomposition for Match Density EstimationCode0
Dance with Flow: Two-in-One Stream Action DetectionCode0
Hallucinating Optical Flow Features for Video ClassificationCode0
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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
4MR-FlowAverage End-Point Error5.38—Unverified
5LiteFlowNet-ftAverage 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
2LiteFlowNet-ftAverage End-Point Error1.6—Unverified
3IRR-PWCAverage End-Point Error1.6—Unverified
4SelFlowAverage End-Point Error1.5—Unverified
5FDFlowNet-ftAverage End-Point Error1.5—Unverified
6PWC-Net + ft - axXivAverage End-Point Error1.5—Unverified
7LiteFlowNet2-ftAverage End-Point Error1.4—Unverified
8LiteFlowNet3-SAverage End-Point Error1.3—Unverified
9LiteFlowNet3Average End-Point Error1.3—Unverified
10MaskFlownetAverage 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