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 401–450 of 2184 papers

TitleStatusHype
Deep 3D World Models for Multi-Image Super-Resolution Beyond Optical Flow—0
SignVTCL: Multi-Modal Continuous Sign Language Recognition Enhanced by Visual-Textual Contrastive Learning—0
VONet: Unsupervised Video Object Learning With Parallel U-Net Attention and Object-wise Sequential VAECode0
Helmholtz-Decomposition and Optical Flow: A new method to characterize GCamP recordings—0
Key-point Guided Deformable Image Manipulation Using Diffusion Model—0
Deep Linear Array Pushbroom Image Restoration: A Degradation Pipeline and Jitter-Aware Restoration NetworkCode1
Dense Optical Flow Estimation Using Sparse Regularizers from Reduced Measurements—0
YOIO: You Only Iterate Once by mining and fusing multiple necessary global information in the optical flow estimation—0
Object-Centric Diffusion for Efficient Video Editing—0
RAVEN: Rethinking Adversarial Video Generation with Efficient Tri-plane Networks—0
RomniStereo: Recurrent Omnidirectional Stereo MatchingCode0
Text-Driven Traffic Anomaly Detection with Temporal High-Frequency Modeling in Driving Videos—0
Group Activity Recognition using Unreliable Tracked Pose—0
DDN-SLAM: Real-time Dense Dynamic Neural Implicit SLAM—0
Flow-Guided Online Stereo Rectification for Wide Baseline Stereo—0
PaReNeRF: Toward Fast Large-scale Dynamic NeRF with Patch-based Reference—0
Implicit Motion FunctionCode1
FlowTrack: Revisiting Optical Flow for Long-Range Dense Tracking—0
FlowDiffuser: Advancing Optical Flow Estimation with Diffusion ModelsCode2
Efficient Meshflow and Optical Flow Estimation from Event CamerasCode1
Projecting Trackable Thermal Patterns for Dynamic Computer Vision—0
Effective Video Mirror Detection with Inconsistent Motion CuesCode0
FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance Head-pose and Facial Expression Features—0
Rethinking RAFT for Efficient Optical FlowCode1
FlowVid: Taming Imperfect Optical Flows for Consistent Video-to-Video Synthesis—0
FlowDA: Unsupervised Domain Adaptive Framework for Optical Flow Estimation—0
X Modality Assisting RGBT Object Tracking—0
Manydepth2: Motion-Aware Self-Supervised Multi-Frame Monocular Depth Estimation in Dynamic ScenesCode1
NTrack: A Multiple-Object Tracker and Dataset for Infield Cotton Boll Counting—0
Appearance-Based Refinement for Object-Centric Motion Segmentation—0
Hierarchical Graph Pattern Understanding for Zero-Shot VOSCode0
Context-Aware Iteration Policy Network for Efficient Optical Flow Estimation—0
Precipitation Downscaling with Spatiotemporal Video Diffusion—0
Early Action Recognition with Action Prototypes—0
Deep Learning Enabled Semantic Communication Systems for Video Transmission—0
Fine Dense Alignment of Image Bursts through Camera Pose and Depth Estimation—0
Memory-Efficient Optical Flow via Radius-Distribution Orthogonal Cost VolumeCode1
SAVE: Protagonist Diversification with Structure Agnostic Video Editing—0
Open-DDVM: A Reproduction and Extension of Diffusion Model for Optical Flow EstimationCode1
Dense Optical Tracking: Connecting the DotsCode2
Un-EvMoSeg: Unsupervised Event-based Independent Motion Segmentation—0
Periodic Vibration Gaussian: Dynamic Urban Scene Reconstruction and Real-time Rendering—0
SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation—0
SigFormer: Sparse Signal-Guided Transformer for Multi-Modal Human Action SegmentationCode0
StreamFlow: Streamlined Multi-Frame Optical Flow Estimation for Video SequencesCode1
Self-Supervised Motion Magnification by Backpropagating Through Optical Flow—0
Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach—0
Flow-Guided Diffusion for Video InpaintingCode2
Highly Detailed and Temporal Consistent Video Stylization via Synchronized Multi-Frame Diffusion—0
Unified Domain Adaptive Semantic SegmentationCode1
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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