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 951–1000 of 2184 papers

TitleStatusHype
Global Temporal Representation based CNNs for Infrared Action Recognition—0
Amora: Black-box Adversarial Morphing Attack—0
Global Motion Understanding in Large-Scale Video Object Segmentation—0
Image-based monitoring of bolt loosening through deep-learning-based integrated detection and tracking—0
DeepRM: Deep Recurrent Matching for 6D Pose Refinement—0
Deep Predictive Video Compression with Bi-directional Prediction—0
Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy—0
GG-SSMs: Graph-Generating State Space Models—0
GFlow: Recovering 4D World from Monocular Video—0
Deep Optical Flow Estimation Via Multi-Scale Correspondence Structure Learning—0
Gesture Recognition with a Focus on Important Actions by Using a Path Searching Method in Weighted Graph—0
Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy—0
Amodal Optical Flow—0
Activity Recognition based on a Magnitude-Orientation Stream Network—0
3D Vehicle Trajectory Reconstruction in Monocular Video Data Using Environment Structure Constraints—0
Improving Deep Video Compression by Resolution-adaptive Flow Coding—0
GeoRefine: Self-Supervised Online Depth Refinement for Accurate Dense Mapping—0
Improving Dynamic Object Interactions in Text-to-Video Generation with AI Feedback—0
Improving Multiple Object Tracking with Optical Flow and Edge Preprocessing—0
Improving Unsupervised Video Object Segmentation via Fake Flow Generation—0
In defense of OSVOS—0
Independent Sign Language Recognition with 3D Body, Hands, and Face Reconstruction—0
Deep Neural Networks in Video Human Action Recognition: A Review—0
GenFlow: Generalizable Recurrent Flow for 6D Pose Refinement of Novel Objects—0
Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness—0
Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models—0
Instance Embedding Transfer to Unsupervised Video Object Segmentation—0
Instance Flow Based Online Multiple Object Tracking—0
Generating, Fast and Slow: Scalable Parallel Video Generation with Video Interface Networks—0
Generalizing to the Open World: Deep Visual Odometry with Online Adaptation—0
Instruct 4D-to-4D: Editing 4D Scenes as Pseudo-3D Scenes Using 2D Diffusion—0
Intention-driven Ego-to-Exo Video Generation—0
Interaction Detection Between Vehicles and Vulnerable Road Users: A Deep Generative Approach with Attention—0
Generalized Video Deblurring for Dynamic Scenes—0
Inter-BMV: Interpolation with Block Motion Vectors for Fast Semantic Segmentation on Video—0
Intermediate and Future Frame Prediction of Geostationary Satellite Imagery With Warp and Refine Network—0
DeepMovie: Using Optical Flow and Deep Neural Networks to Stylize Movies—0
Avoiding Degeneracy for Monocular Visual SLAM with Point and Line Features—0
Deep Motion Prior for Weakly-Supervised Temporal Action Localization—0
Invariant-based Mapping of Space During General Motion of an Observer—0
GelFlow: Self-supervised Learning of Optical Flow for Vision-Based Tactile Sensor Displacement Measurement—0
Investigation of Factorized Optical Flows as Mid-Level Representations—0
GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection—0
Investigation of moving objects through atmospheric turbulence from a non-stationary platform—0
Deep Motion Features for Visual Tracking—0
A Video-Based Method for Objectively Rating Ataxia—0
Gaze Stabilization for Humanoid Robots: a Comprehensive Framework—0
GaussianFlow: Splatting Gaussian Dynamics for 4D Content Creation—0
GateHUB: Gated History Unit with Background Suppression for Online Action Detection—0
Deep Motion Boundary Detection—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