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 1201–1250 of 2184 papers

TitleStatusHype
Conditional Video Generation Using Action-Appearance Captions—0
Computational Motility Tracking of Calcium Dynamics in Toxoplasma gondii—0
MeNToS: Tracklets Association with a Space-Time Memory Network—0
Merry Go Round: Rotate a Frame and Fool a DNN—0
MESD: Exploring Optical Flow Assessment on Edge of Motion Objects with Motion Edge Structure Difference—0
Mesh Guided One-shot Face Reenactment using Graph Convolutional Networks—0
Meta-Interpolation: Time-Arbitrary Frame Interpolation via Dual Meta-Learning—0
Segmenting the motion components of a video: A long-term unsupervised model—0
Compressive Coded Aperture Keyed Exposure Imaging with Optical Flow Reconstruction—0
Compressible Motion Fields—0
Compressed Video Action Recognition with Refined Motion Vector—0
What Matters in Detecting AI-Generated Videos like Sora?—0
Micro-expression detection in long videos using optical flow and recurrent neural networks—0
Comparison of Optimization Methods in Optical Flow Estimation—0
MicroFlow: Domain-Specific Optical Flow for Ground Deformation Estimation in Seismic Events—0
Microstructure Surface Reconstruction from SEM Images: An Alternative to Digital Image Correlation (DIC)—0
Unsupervised Optical Flow Estimation with Dynamic Timing Representation for Spike Camera—0
Exploring the Impacts from Datasets to Monocular Depth Estimation (MDE) Models with MineNavi—0
Comparing Optical Flow and Deep Learning to Enable Computationally Efficient Traffic Event Detection with Space-Filling Curves—0
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices—0
MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation—0
Mixed Reality Depth Contour Occlusion Using Binocular Similarity Matching and Three-dimensional Contour Optimisation—0
Compact CNN for Indexing Egocentric Videos—0
Combining Stereo Disparity and Optical Flow for Basic Scene Flow—0
Combining Spatio-Temporal Appearance Descriptors and Optical Flow for Human Action Recognition in Video Data—0
MM-ViT: Multi-Modal Video Transformer for Compressed Video Action Recognition—0
NOLO: Navigate Only Look Once—0
Modality Compensation Network: Cross-Modal Adaptation for Action Recognition—0
ColoristaNet for Photorealistic Video Style Transfer—0
Colonoscopy 3D Video Dataset with Paired Depth from 2D-3D Registration—0
Modeling Spatio-temporal Dynamical Systems with Neural Discrete Learning and Levels-of-Experts—0
Modeling Spatio-Temporal Human Track Structure for Action Localization—0
Modelling Human Visual Motion Processing with Trainable Motion Energy Sensing and a Self-attention Network—0
Unsupervised Region-Growing Network for Object Segmentation in Atmospheric Turbulence—0
MODETR: Moving Object Detection with Transformers—0
Mojito: Motion Trajectory and Intensity Control for Video Generation—0
Coherent Motion Segmentation in Moving Camera Videos using Optical Flow Orientations—0
MONA: Moving Object Detection from Videos Shot by Dynamic Camera—0
MoNet: Deep Motion Exploitation for Video Object Segmentation—0
MonoComb: A Sparse-to-Dense Combination Approach for Monocular Scene Flow—0
CNN in MRF: Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRF—0
Monocular Depth Guided Occlusion-Aware Disparity Refinement via Semi-supervised Learning in Laparoscopic Images—0
Monocular Piecewise Depth Estimation in Dynamic Scenes by Exploiting Superpixel Relations—0
MonoMobility: Zero-Shot 3D Mobility Analysis from Monocular Videos—0
TempNet -- Temporal Super Resolution of Radar Rainfall Products with Residual CNNs—0
Mono-Stixels: Monocular depth reconstruction of dynamic street scenes—0
Accelerated Optimization in the PDE Framework: Formulations for the Manifold of Diffeomorphisms—0
MotiF: Making Text Count in Image Animation with Motion Focal Loss—0
MotionAgent: Fine-grained Controllable Video Generation via Motion Field Agent—0
Motion and Context-Aware Audio-Visual Conditioned Video Prediction—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