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 451–500 of 2184 papers

TitleStatusHype
MoVideo: Motion-Aware Video Generation with Diffusion Models—0
Invariant-based Mapping of Space During General Motion of an Observer—0
A Video-Based Activity Classification of Human Pickers in Agriculture—0
Efficient Temporally-Aware DeepFake Detection using H.264 Motion Vectors—0
A Lightweight Recurrent Aggregation Network for Satellite Video Super-ResolutionCode1
Non-Contact Breathing Rate Detection Using Optical Flow—0
Improve Myocardial Strain Estimation based on Deformable Groupwise Registration with a Locally Low-Rank Dissimilarity Metric—0
Amodal Optical Flow—0
Exploiting Inductive Biases in Video Modeling through Neural CDEs—0
Dual-Stream Attention Transformers for Sewer Defect ClassificationCode0
ADFactory: An Effective Framework for Generalizing Optical Flow with Nerf—0
Unsupervised Region-Growing Network for Object Segmentation in Atmospheric Turbulence—0
CCMR: High Resolution Optical Flow Estimation via Coarse-to-Fine Context-Guided Motion ReasoningCode1
The Background Also Matters: Background-Aware Motion-Guided Objects Discovery—0
EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-SupervisionCode2
Infusion: internal diffusion for inpainting of dynamic textures and complex motionCode1
Event-based Background-Oriented SchlierenCode1
LatentWarp: Consistent Diffusion Latents for Zero-Shot Video-to-Video Translation—0
FLODCAST: Flow and Depth Forecasting via Multimodal Recurrent Architectures—0
Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical FlowCode0
Salient Object Detection in RGB-D VideosCode1
Subtle Signals: Video-based Detection of Infant Non-nutritive Sucking as a Neurodevelopmental CueCode0
Breaking of brightness consistency in optical flow with a lightweight CNN networkCode2
Guidance system for Visually Impaired Persons using Deep Learning and Optical flow—0
Linguistically Motivated Sign Language SegmentationCode1
RGM: A Robust Generalizable Matching ModelCode1
HSTR-Net: Reference Based Video Super-resolution with Dual Cameras—0
Uncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurements—0
Effortless Cross-Platform Video Codec: A Codebook-Based Method—0
Flow Dynamics Correction for Action Recognition—0
Smart City Transportation: Deep Learning Ensemble Approach for Traffic Accident Detection—0
Tabletop Transparent Scene Reconstruction via Epipolar-Guided Optical Flow with Monocular Depth Completion Prior—0
Detecting Moving Objects Using a Novel Optical-Flow-Based Range-Independent Invariant—0
Time-based Mapping of Space Using Visual Motion Invariants—0
TIDE: Temporally Incremental Disparity Estimation via Pattern Flow in Structured Light SystemCode0
A Spatial-Temporal Dual-Mode Mixed Flow Network for Panoramic Video Salient Object Detection—0
Zero-Shot Open-Vocabulary Tracking with Large Pre-Trained Models—0
FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editingCode2
Improving Discriminative Multi-Modal Learning with Large-Scale Pre-Trained Models—0
UFD-PRiME: Unsupervised Joint Learning of Optical Flow and Stereo Depth through Pixel-Level Rigid Motion Estimation—0
WAIT: Feature Warping for Animation to Illustration video Translation using GANsCode1
Skin the sheep not only once: Reusing Various Depth Datasets to Drive the Learning of Optical Flow—0
Segmenting the motion components of a video: A long-term unsupervised model—0
Ground-A-Video: Zero-shot Grounded Video Editing using Text-to-image Diffusion ModelsCode1
Win-Win: Training High-Resolution Vision Transformers from Two Windows—0
GAFlow: Incorporating Gaussian Attention into Optical FlowCode1
Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback—0
Learning Dense Flow Field for Highly-accurate Cross-view Camera Localization—0
Treating Motion as Option with Output Selection for Unsupervised Video Object SegmentationCode1
RPEFlow: Multimodal Fusion of RGB-PointCloud-Event for Joint Optical Flow and Scene Flow EstimationCode1
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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