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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1501–1550 of 2184 papers

TitleStatusHype
EMatch: A Unified Framework for Event-based Optical Flow and Stereo Matching—0
Unifying (Machine) Vision via Counterfactual World Modeling—0
PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation—0
Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation—0
Unmasking Deepfakes: Masked Autoencoding Spatiotemporal Transformers for Enhanced Video Forgery Detection—0
Unsupervised 4D Cardiac Motion Tracking with Spatiotemporal Optical Flow Networks—0
Unsupervised convolutional neural networks for motion estimation—0
Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow—0
Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes—0
Unsupervised Deformable Ultrasound Image Registration and Its Application for Vessel Segmentation—0
Unsupervised Domain Adaptation by Optical Flow Augmentation in Semantic Segmentation—0
Unsupervised Fish Trajectory Tracking and Segmentation—0
Unsupervised Flow Refinement near Motion Boundaries—0
Unsupervised Hierarchical Domain Adaptation for Adverse Weather Optical Flow—0
Unsupervised Learning of Dense Optical Flow, Depth and Egomotion from Sparse Event Data—0
Unsupervised Learning of Depth, Camera Pose and Optical Flow from Monocular Video—0
Unsupervised Learning of Multi-Frame Optical Flow with Occlusions—0
Unsupervised learning of object frames by dense equivariant image labelling—0
Unsupervised Learning of Optical Flow with Deep Feature Similarity—0
Unsupervised Learning Optical Flow in Multi-frame Dynamic Environment Using Temporal Dynamic Modeling—0
Unsupervised Motion Representation Enhanced Network for Action Recognition—0
Unsupervised motion saliency map estimation based on optical flow inpainting—0
Segmenting the motion components of a video: A long-term unsupervised model—0
Unsupervised Optical Flow Estimation with Dynamic Timing Representation for Spike Camera—0
Unsupervised Region-Growing Network for Object Segmentation in Atmospheric Turbulence—0
Unsupervised Segmentation of Fire and Smoke from Infra-Red Videos—0
Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Features—0
Unsupervised Video Object Segmentation using Motion Saliency-Guided Spatio-Temporal Propagation—0
USegScene: Unsupervised Learning of Depth, Optical Flow and Ego-Motion with Semantic Guidance and Coupled Networks—0
Using Motion Cues to Supervise Single-Frame Body Pose and Shape Estimation in Low Data Regimes—0
VanGogh: A Unified Multimodal Diffusion-based Framework for Video Colorization—0
Vector-Symbolic Architecture for Event-Based Optical Flow—0
Vertical Landing for Micro Air Vehicles using Event-Based Optical Flow—0
VidCRAFT3: Camera, Object, and Lighting Control for Image-to-Video Generation—0
Video Action Recognition Using spatio-temporal optical flow video frames—0
Video Anomaly Detection By The Duality Of Normality-Granted Optical Flow—0
Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach—0
Video-based Person Re-Identification using Gated Convolutional Recurrent Neural Networks—0
Video-based Person Re-identification with Two-stream Convolutional Network and Co-attentive Snippet Embedding—0
VideoCapsuleNet: A Simplified Network for Action Detection—0
Video Classification With CNNs: Using The Codec As A Spatio-Temporal Activity Sensor—0
Video Compressive Sensing for Spatial Multiplexing Cameras using Motion-Flow Models—0
Video ControlNet: Towards Temporally Consistent Synthetic-to-Real Video Translation Using Conditional Image Diffusion Models—0
Video Deblurring via Semantic Segmentation and Pixel-Wise Non-Linear Kernel—0
Video Desnowing and Deraining Based on Matrix Decomposition—0
Video Diffusion Models are Strong Video Inpainter—0
Video Forgery Detection Using Multiple Cues on Fusion of EfficientNet and Swin Transformer—0
Video Frame Interpolation via Structure-Motion based Iterative Fusion—0
Video Frame Interpolation with Stereo Event and Intensity Camera—0
Video Interpolation and Prediction with Unsupervised Landmarks—0
Show:102550
← PrevPage 31 of 44Next →
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