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 251–300 of 2184 papers

TitleStatusHype
A Simple Baseline for Video Restoration with Grouped Spatial-temporal ShiftCode1
Learning to Estimate and Refine Fluid Motion with Physical DynamicsCode1
Enhanced Bi-directional Motion Estimation for Video Frame InterpolationCode1
GradICON: Approximate Diffeomorphisms via Gradient Inverse ConsistencyCode1
SKFlow: Learning Optical Flow with Super KernelsCode1
Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video RestorationCode1
Unsupervised Segmentation in Real-World Images via Spelke Object InferenceCode1
Multiview Stereo with Cascaded Epipolar RAFTCode1
D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion PredictionCode1
ClothFormer:Taming Video Virtual Try-on in All ModuleCode1
A New Dataset and Transformer for Stereoscopic Video Super-ResolutionCode1
Deep Equilibrium Optical Flow EstimationCode1
BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable AlignmentCode1
Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference ModelingCode1
Many-to-many Splatting for Efficient Video Frame InterpolationCode1
Modeling Motion with Multi-Modal Features for Text-Based Video SegmentationCode1
Learning Video Salient Object Detection Progressively from Unlabeled VideosCode1
DIP: Deep Inverse Patchmatch for High-Resolution Optical FlowCode1
CRAFT: Cross-Attentional Flow Transformer for Robust Optical FlowCode1
Abstract Flow for Temporal Semantic Segmentation on the Permutohedral LatticeCode1
Dense Continuous-Time Optical Flow from Events and FramesCode1
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical FlowCode1
Global Matching with Overlapping Attention for Optical Flow EstimationCode1
Beyond a Video Frame Interpolator: A Space Decoupled Learning Approach to Continuous Image TransitionCode1
Bringing Rolling Shutter Images Alive with Dual Reversed DistortionCode1
Optical Flow Training under Limited Label Budget via Active LearningCode1
Optical Flow Based Motion Detection for Autonomous DrivingCode1
Spatio-temporal Vision Transformer for Super-resolution MicroscopyCode1
PanoFlow: Learning 360° Optical Flow for Surrounding Temporal UnderstandingCode1
Depth-Cooperated Trimodal Network for Video Salient Object DetectionCode1
Learning Optical Flow with Adaptive Graph ReasoningCode1
CSFlow: Learning Optical Flow via Cross Strip Correlation for Autonomous DrivingCode1
Self-Supervised Deep Blind Video Super-ResolutionCode1
Learning Temporally and Semantically Consistent Unpaired Video-to-video Translation Through Pseudo-Supervision From Synthetic Optical FlowCode1
Motion-Focused Contrastive Learning of Video RepresentationsCode1
Flow-Guided Sparse Transformer for Video DeblurringCode1
EM-driven unsupervised learning for efficient motion segmentationCode1
E2(GO)MOTION: Motion Augmented Event Stream for Egocentric Action RecognitionCode1
Learning Optical Flow With Kernel Patch AttentionCode1
Inertia-Guided Flow Completion and Style Fusion for Video InpaintingCode1
360° Optical Flow using Tangent ImagesCode1
Learning Cross-Scale Weighted Prediction for Efficient Neural Video CompressionCode1
Real-Time Optical Flow for Vehicular Perception with Low- and High-Resolution Event CamerasCode1
Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical FlowCode1
Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly DetectionCode1
E^2(GO)MOTION: Motion Augmented Event Stream for Egocentric Action RecognitionCode1
Video Frame Interpolation without Temporal PriorsCode1
Conditional Object-Centric Learning from VideoCode1
Monocular Arbitrary Moving Object Discovery and SegmentationCode1
FAMINet: Learning Real-time Semi-supervised Video Object Segmentation with Steepest Optimized Optical FlowCode1
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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