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 2051–2100 of 2184 papers

TitleStatusHype
Every Frame Counts: Joint Learning of Video Segmentation and Optical Flow—0
TransFlow: Transformer as Flow Learner—0
Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding—0
DressRecon: Freeform 4D Human Reconstruction from Monocular Video—0
TransFlow: Unsupervised Motion Flow by Joint Geometric and Pixel-level Estimation—0
EVIMO2: An Event Camera Dataset for Motion Segmentation, Optical Flow, Structure from Motion, and Visual Inertial Odometry in Indoor Scenes with Monocular or Stereo Algorithms—0
EV-LayerSegNet: Self-supervised Motion Segmentation using Event Cameras—0
DO3D: Self-supervised Learning of Decomposed Object-aware 3D Motion and Depth from Monocular Videos—0
EV-MGRFlowNet: Motion-Guided Recurrent Network for Unsupervised Event-based Optical Flow with Hybrid Motion-Compensation Loss—0
Evolution of Robust High Speed Optical-Flow-Based Landing for Autonomous MAVs—0
Transframer: Arbitrary Frame Prediction with Generative Models—0
Evolvement Constrained Adversarial Learning for Video Style Transfer—0
Evolving Losses for Unlabeled Video Representation Learning—0
DNN-Driven Compressive Offloading for Edge-Assisted Semantic Video Segmentation—0
Explicit Depth-Aware Blurry Video Frame Interpolation Guided by Differential Curves—0
DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition—0
Explicit Motion Handling and Interactive Prompting for Video Camouflaged Object Detection—0
Explicit Spatiotemporal Joint Relation Learning for Tracking Human Pose—0
Exploiting Correspondences with All-pairs Correlations for Multi-view Depth Estimation—0
ADFactory: An Effective Framework for Generalizing Optical Flow with Nerf—0
Exploiting Inductive Biases in Video Modeling through Neural CDEs—0
Exploiting Inter-Frame Regional Correlation for Efficient Action Recognition—0
Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots—0
Exploiting Semantic Information and Deep Matching for Optical Flow—0
DistractFlow: Improving Optical Flow Estimation via Realistic Distractions and Pseudo-Labeling—0
Trear: Transformer-based RGB-D Egocentric Action Recognition—0
Exploring Human Crowd Patterns and Categorization in Video Footage for Enhanced Security and Surveillance using Computer Vision and Machine Learning—0
A Deep-structured Conditional Random Field Model for Object Silhouette Tracking—0
Exploring More from Multiple Gait Modalities for Human Identification—0
Exploring Motion Ambiguity and Alignment for High-Quality Video Frame Interpolation—0
Exploring Optical-Flow-Guided Motion and Detection-Based Appearance for Temporal Sentence Grounding—0
Exploring Optical Flow Inclusion into nnU-Net Framework for Surgical Instrument Segmentation—0
Exploring Self-Attention for Visual Odometry—0
Distinguishing Posed and Spontaneous Smiles by Facial Dynamics—0
AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for Human Action Recognition in Videos—0
Exploring the Common Appearance-Boundary Adaptation for Nighttime Optical Flow—0
Exploring Weak Stabilization for Motion Feature Extraction—0
Exposure Fusion for Hand-held Camera Inputs with Optical Flow and PatchMatch—0
TartanGround: A Large-Scale Dataset for Ground Robot Perception and Navigation—0
Extending Visual Dynamics for Video-to-Music Generation—0
Distinguish Any Fake Videos: Unleashing the Power of Large-scale Data and Motion Features—0
Distance Surface for Event-Based Optical Flow—0
Face Animation with Multiple Source Images—0
Face Flow—0
Triple-stream Deep Metric Learning of Great Ape Behavioural Actions—0
Discrete MRF Inference of Marginal Densities for Non-uniformly Discretized Variable Space—0
Trustworthy Self-Attention: Enabling the Network to Focus Only on the Most Relevant References—0
Fall Detector Adapted to Nursing Home Needs through an Optical-Flow based CNN—0
Discovering Hidden Physics Behind Transport Dynamics—0
Fast and Accurate Optical Flow based Depth Map Estimation from Light Fields—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