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 1101–1150 of 2184 papers

TitleStatusHype
Learning semantical dynamics and spatiotemporal collaboration for human pose estimation in video—0
Learning Semantic-Aware Dynamics for Video Prediction—0
Learning Similarity Metrics for Dynamic Scene Segmentation—0
Dance Any Beat: Blending Beats with Visuals in Dance Video Generation—0
Learning Spatiotemporal Features for Infrared Action Recognition with 3D Convolutional Neural Networks—0
Learning Spatio-Temporal Features with Two-Stream Deep 3D CNNs for Lipreading—0
What happens in Face during a facial expression? Using data mining techniques to analyze facial expression motion vectors—0
Learning Temporal 3D Semantic Scene Completion via Optical Flow Guidance—0
DAISY Filter Flow: A Generalized Discrete Approach to Dense Correspondences—0
Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information—0
What If We Do Not Have Multiple Videos of the Same Action? -- Video Action Localization Using Web Images—0
Learning the Local Statistics of Optical Flow—0
Learning the Matching Function—0
Learning to Detect Motion Boundaries—0
CTRL-GS: Cascaded Temporal Residue Learning for 4D Gaussian Splatting—0
Crowd Flow Segmentation in Compressed Domain using CRF—0
Learning to Extract Motion from Videos in Convolutional Neural Networks—0
Cross Pixel Optical Flow Similarity for Self-Supervised Learning—0
Cross-Modal Message Passing for Two-stream Fusion—0
Learning To Segment Dominant Object Motion From Watching Videos—0
Learning to Segment Moving Objects—0
15 Keypoints Is All You Need—0
Learning to Track Any Points from Human Motion—0
Crossing the Road Without Traffic Lights: An Android-based Safety Device—0
Learning to Transfer Visual Effects from Videos to Images—0
Learning V1 Simple Cells with Vector Representation of Local Content and Matrix Representation of Local Motion—0
Cross-Enhancement Transform Two-Stream 3D ConvNets for Action Recognition—0
Learning Video Stabilization Using Optical Flow—0
Learning View-Invariant Features for Person Identification in Temporally Synchronized Videos Taken by Wearable Cameras—0
Learning Visual Voice Activity Detection with an Automatically Annotated Dataset—0
Learning without Prejudice: Avoiding Bias in Webly-Supervised Action Recognition—0
Learn to Model Motion from Blurry Footages—0
Left Ventricle Segmentation via Optical-Flow-Net from Short-axis Cine MRI: Preserving the Temporal Coherence of Cardiac Motion—0
Cross domain knowledge compression in realtime optical flow prediction on ultrasound sequences—0
Less is More: Micro-expression Recognition from Video using Apex Frame—0
Let Occ Flow: Self-Supervised 3D Occupancy Flow Prediction—0
Unsupervised Learning of Dense Optical Flow, Depth and Egomotion from Sparse Event Data—0
Level set based particle filter driven by optical flow: an application to track the salt boundary from X-ray CT time-series—0
Unsupervised Learning of Depth, Camera Pose and Optical Flow from Monocular Video—0
Leveraging Motion Priors in Videos for Improving Human Segmentation—0
Covariance of Motion and Appearance Featuresfor Spatio Temporal Recognition Tasks—0
Leveraging Photometric Consistency over Time for Sparsely Supervised Hand-Object Reconstruction—0
Coupled Recurrent Network (CRN)—0
Leveraging Structural Context Models and Ranking Score Fusion for Human Interaction Prediction—0
Leveraging the HW/SW Optimizations and Ecosystems that Drive the AI Revolution—0
Coupled Generative Adversarial Network for Continuous Fine-grained Action Segmentation—0
Light Field Super-Resolution using a Low-Rank Prior and Deep Convolutional Neural Networks—0
Light Weight Color Image Warping with Inter-Channel Information—0
Counting people from above: Airborne video based crowd analysis—0
CoT-AMFlow: Adaptive Modulation Network with Co-Teaching Strategy for Unsupervised Optical Flow Estimation—0
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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