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 1951–2000 of 2184 papers

TitleStatusHype
Adverse Weather Optical Flow: Cumulative Homogeneous-Heterogeneous Adaptation—0
Toward Pedestrian Head Tracking: A Benchmark Dataset and an Information Fusion Network—0
Towards Accurate Human Pose Estimation in Videos of Crowded Scenes—0
RenderBender: A Survey on Adversarial Attacks Using Differentiable Rendering—0
Towards a Generalizable Bimanual Foundation Policy via Flow-based Video Prediction—0
Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers—0
Advancing Auto-Regressive Continuation for Video Frames—0
A Dual Fast and Slow Feature Interaction in Biologically Inspired Visual Recognition of Human Action—0
Towards Efficient Real-Time Video Motion Transfer via Generative Time Series Modeling—0
Towards Mobile Sensing with Event Cameras on High-agility Resource-constrained Devices: A Survey—0
A Discrete Scheme for Computing Image's Weighted Gaussian Curvature—0
Edge SLAM: Edge Points Based Monocular Visual SLAM—0
Edit as You See: Image-guided Video Editing via Masked Motion Modeling—0
Volumetric Flow Estimation for Incompressible Fluids Using the Stationary Stokes Equations—0
Towards Visual Ego-motion Learning in Robots—0
Efficient Correlation Volume Sampling for Ultra-High-Resolution Optical Flow Estimation—0
Efficient Dynamic Scene Deblurring Using Spatially Variant Deconvolution Network With Optical Flow Guided Training—0
EDeNN: Event Decay Neural Networks for low latency vision—0
3D-CNN for Facial Micro- and Macro-expression Spotting on Long Video Sequences using Temporal Oriented Reference Frame—0
Efficient Masked AutoEncoder for Video Object Counting and A Large-Scale Benchmark—0
EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation—0
Efficient Neuromorphic Signal Processing with Loihi 2—0
Early Action Recognition with Action Prototypes—0
Dynamic semantic VSLAM with known and unknown objects—0
Efficient Sparse-to-Dense Optical Flow Estimation Using a Learned Basis and Layers—0
Efficient Temporally-Aware DeepFake Detection using H.264 Motion Vectors—0
Efficient Two-Stream Motion and Appearance 3D CNNs for Video Classification—0
Efficient Unsupervised Video Object Segmentation Network Based on Motion Guidance—0
Dynamic Scene Deblurring using a Locally Adaptive Linear Blur Model—0
Efficient Video Segmentation Models with Per-frame Inference—0
Efficient Video Semantic Segmentation with Labels Propagation and Refinement—0
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation—0
Effortless Cross-Platform Video Codec: A Codebook-Based Method—0
Egocentric Height Estimation—0
Dynamic Facial Expression Recognition under Partial Occlusion with Optical Flow Reconstruction—0
Egocentric Vision Language Planning—0
Ego-motion and Surrounding Vehicle State Estimation Using a Monocular Camera—0
Track, Check, Repeat: An EM Approach to Unsupervised Tracking—0
Ego-motion Sensor for Unmanned Aerial Vehicles Based on a Single-Board Computer—0
EMAG: Ego-motion Aware and Generalizable 2D Hand Forecasting from Egocentric Videos—0
Dynamic Event-based Optical Identification and Communication—0
Dynamic Dense RGB-D SLAM using Learning-based Visual Odometry—0
Emotion-Controllable Generalized Talking Face Generation—0
Emotion Recognition from the perspective of Activity Recognition—0
EMoTive: Event-guided Trajectory Modeling for 3D Motion Estimation—0
EndoFlow-SLAM: Real-Time Endoscopic SLAM with Flow-Constrained Gaussian Splatting—0
Dynamical optical flow of saliency maps for predicting visual attention—0
Endoscopic Depth Measurement and Super-Spectral-Resolution Imaging—0
Endo-TTAP: Robust Endoscopic Tissue Tracking via Multi-Facet Guided Attention and Hybrid Flow-point Supervision—0
End to end collision avoidance based on optical flow and neural networks—0
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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
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