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 301–350 of 2184 papers

TitleStatusHype
Detail-Enhanced Intra- and Inter-modal Interaction for Audio-Visual Emotion Recognition—0
Motion Segmentation for Neuromorphic Aerial SurveillanceCode0
Distinguish Any Fake Videos: Unleashing the Power of Large-scale Data and Motion Features—0
Neuroexplicit Diffusion Models for Inpainting of Optical Flow Fields—0
SEA-RAFT: Simple, Efficient, Accurate RAFT for Optical FlowCode4
OpFlowTalker: Realistic and Natural Talking Face Generation via Optical Flow Guidance—0
MotionCraft: Physics-based Zero-Shot Video GenerationCode1
Weakly supervised alignment and registration of MR-CT for cervical cancer radiotherapy—0
Rethink Predicting the Optical Flow with the Kinetics PerspectiveCode0
GestFormer: Multiscale Wavelet Pooling Transformer Network for Dynamic Hand Gesture RecognitionCode1
MicroBundlePillarTrack: A Python package for automated segmentation, tracking, and analysis of pillar deflection in cardiac microbundlesCode0
Higher-order Spatio-temporal Physics-incorporated Graph Neural Network for Multivariate Time Series ImputationCode1
Dance Any Beat: Blending Beats with Visuals in Dance Video Generation—0
EchoTracker: Advancing Myocardial Point Tracking in EchocardiographyCode2
Vector-Symbolic Architecture for Event-Based Optical Flow—0
NGD-SLAM: Towards Real-Time Dynamic SLAM without GPUCode3
DeVOS: Flow-Guided Deformable Transformer for Video Object Segmentation—0
Global Motion Understanding in Large-Scale Video Object Segmentation—0
A Survey on Backbones for Deep Video Action Recognition—0
Multi-scale Bottleneck Transformer for Weakly Supervised Multimodal Violence DetectionCode1
Diffeomorphic Template Registration for Atmospheric Turbulence Mitigation—0
Hierarchical Space-Time Attention for Micro-Expression RecognitionCode1
UnSAMFlow: Unsupervised Optical Flow Guided by Segment Anything ModelCode2
DreamScene4D: Dynamic Multi-Object Scene Generation from Monocular VideosCode3
Self-Supervised Learning for Real-World Super-Resolution from Dual and Multiple Zoomed ObservationsCode2
RAPIDFlow: Recurrent Adaptable Pyramids with Iterative Decoding for Efficient Optical Flow EstimationCode4
Semantically Consistent Video Inpainting with Conditional Diffusion Models—0
ν-DBA: Neural Implicit Dense Bundle Adjustment Enables Image-Only Driving Scene Reconstruction—0
Event-based Video Frame Interpolation with Edge Guided Motion Refinement—0
Motor Focus: Fast Ego-Motion Prediction for Assistive Visual NavigationCode0
Deep-learning Optical Flow Outperforms PIV in Obtaining Velocity Fields from Active NematicsCode0
Multi-Session SLAM with Differentiable Wide-Baseline Pose OptimizationCode2
FlowMap: High-Quality Camera Poses, Intrinsics, and Depth via Gradient DescentCode4
Structure-Aware Human Body Reshaping with Adaptive Affinity-Graph NetworkCode1
Attack on Scene Flow using Point CloudsCode0
Turb-Seg-Res: A Segment-then-Restore Pipeline for Dynamic Videos with Atmospheric Turbulence—0
Weakly Supervised LiDAR Semantic Segmentation via Scatter Image Annotation—0
3D Multi-frame Fusion for Video StabilizationCode1
Moving Object Segmentation: All You Need Is SAM (and Flow)Code3
TempBEV: Improving Learned BEV Encoders with Combined Image and BEV Space Temporal Aggregation—0
Vision-based control for landing an aerial vehicle on a marine vessel—0
Improving Bracket Image Restoration and Enhancement with Flow-guided Alignment and Enhanced Feature Aggregation—0
Table tennis ball spin estimation with an event camera—0
FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance, Head-pose, and Facial Expression Features—0
SEVD: Synthetic Event-based Vision Dataset for Ego and Fixed Traffic PerceptionCode1
SciFlow: Empowering Lightweight Optical Flow Models with Self-Cleaning Iterations—0
Chaos in Motion: Unveiling Robustness in Remote Heart Rate Measurement through Brain-Inspired Skin Tracking—0
MemFlow: Optical Flow Estimation and Prediction with Memory—0
Salient Sparse Visual Odometry With Pose-Only Supervision—0
BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression TasksCode1
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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
4LiteFlowNet-ftAverage End-Point Error5.38—Unverified
5MR-FlowAverage 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
2IRR-PWCAverage End-Point Error1.6—Unverified
3LiteFlowNet-ftAverage End-Point Error1.6—Unverified
4PWC-Net + ft - axXivAverage End-Point Error1.5—Unverified
5FDFlowNet-ftAverage End-Point Error1.5—Unverified
6SelFlowAverage End-Point Error1.5—Unverified
7LiteFlowNet2-ftAverage End-Point Error1.4—Unverified
8LiteFlowNet3Average End-Point Error1.3—Unverified
9LiteFlowNet3-SAverage End-Point Error1.3—Unverified
10MaskFlownet-SAverage 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