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 551–600 of 2184 papers

TitleStatusHype
MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised Learning of Motion and Content Features—0
Pūioio: On-device Real-Time Smartphone-Based Automated Exercise Repetition Counting System—0
BatMobility: Towards Flying Without Seeing for Autonomous DronesCode0
Deficiency-Aware Masked Transformer for Video InpaintingCode1
Video Frame Interpolation with Stereo Event and Intensity Camera—0
CoTracker: It is Better to Track TogetherCode4
Multimodal Distillation for Egocentric Action RecognitionCode1
Unsupervised Optical Flow Estimation with Dynamic Timing Representation for Spike Camera—0
Close-up View synthesis by Interpolating Optical Flow—0
Towards Anytime Optical Flow Estimation with Event CamerasCode1
Offline and Online Optical Flow Enhancement for Deep Video Compression—0
Latent Graph Attention for Enhanced Spatial Context—0
FTFDNet: Learning to Detect Talking Face Video Manipulation with Tri-Modality Interaction—0
Spatio-Temporal Perception-Distortion Trade-off in Learned Video SRCode0
The Drunkard's Odometry: Estimating Camera Motion in Deforming ScenesCode1
Delving into Crispness: Guided Label Refinement for Crisp Edge DetectionCode1
S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural NetworksCode0
Shape-Constraint Recurrent Flow for 6D Object Pose EstimationCode1
Unsupervised Deformable Ultrasound Image Registration and Its Application for Vessel Segmentation—0
Online Unsupervised Video Object Segmentation via Contrastive Motion ClusteringCode0
CANDID: Correspondence AligNment for Deep-burst Image Denoising—0
Deformation Monitoring of Tunnel using Phase-based Motion Magnification and Optical Flow—0
Infinite Photorealistic Worlds using Procedural GenerationCode5
SplatFlow: Learning Multi-frame Optical Flow via SplattingCode1
1st Solution Places for CVPR 2023 UG^2+ Challenge Track 2.1-Text Recognition through Atmospheric TurbulenceCode0
Neural World Models for Computer Vision—0
MMASD: A Multimodal Dataset for Autism Intervention AnalysisCode1
Unmasking Deepfakes: Masked Autoencoding Spatiotemporal Transformers for Enhanced Video Forgery Detection—0
DIFT: Dynamic Iterative Field Transforms for Memory Efficient Optical Flow—0
Tracking Everything Everywhere All at OnceCode4
FlowFormer: A Transformer Architecture and Its Masked Cost Volume Autoencoding for Optical Flow—0
Best of Both Worlds: Hybrid SNN-ANN Architecture for Event-based Optical Flow Estimation—0
The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation—0
Unifying (Machine) Vision via Counterfactual World Modeling—0
FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses via Pixel-Aligned Scene Flow—0
A Multi-Modal Transformer Network for Action Detection—0
Video ControlNet: Towards Temporally Consistent Synthetic-to-Real Video Translation Using Conditional Image Diffusion Models—0
Mining Negative Temporal Contexts For False Positive Suppression In Real-Time Ultrasound Lesion DetectionCode1
Deep Neural Networks in Video Human Action Recognition: A Review—0
AutoDepthNet: High Frame Rate Depth Map Reconstruction using Commodity Depth and RGB Cameras—0
Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback LearningCode2
FEDORA: Flying Event Dataset fOr Reactive behAvior—0
A Fast and Accurate Optical Flow Camera for Resource-Constrained Edge Applications—0
Parallelizing Optical Flow Estimation on an Ultra-Low Power RISC-V Cluster for Nano-UAV Navigation—0
SCP: Soft Conditional Prompt Learning for Aerial Video Action Recognition—0
SIDAR: Synthetic Image Dataset for Alignment & RestorationCode0
S^3Track: Self-supervised Tracking with Soft Assignment Flow—0
IDO-VFI: Identifying Dynamics via Optical Flow Guidance for Video Frame Interpolation with EventsCode0
Modelling Human Visual Motion Processing with Trainable Motion Energy Sensing and a Self-attention Network—0
EV-MGRFlowNet: Motion-Guided Recurrent Network for Unsupervised Event-based Optical Flow with Hybrid Motion-Compensation Loss—0
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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
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