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
EMoTive: Event-guided Trajectory Modeling for 3D Motion Estimation—0
ST-FlowNet: An Efficient Spiking Neural Network for Event-Based Optical Flow Estimation—0
Markerless Tracking-Based Registration for Medical Image Motion Correction—0
Flow-NeRF: Joint Learning of Geometry, Poses, and Dense Flow within Unified Neural Representations—0
Investigation of Frame Differences as Motion Cues for Video Object Segmentation—0
Feature Alignment with Equivariant Convolutions for Burst Image Super-Resolution—0
Bridge Frame and Event: Common Spatiotemporal Fusion for High-Dynamic Scene Optical Flow—0
MambaFlow: A Mamba-Centric Architecture for End-to-End Optical Flow Estimation—0
Stereo Any Video: Temporally Consistent Stereo Matching—0
ReynoldsFlow: Exquisite Flow Estimation via Reynolds Transport TheoremCode0
BAT: Learning Event-based Optical Flow with Bidirectional Adaptive Temporal CorrelationCode0
Anomaly detection in non-stationary videos using time-recursive differencing network based prediction—0
AI-Driven Relocation Tracking in Dynamic Kitchen EnvironmentsCode0
Learning Temporal 3D Semantic Scene Completion via Optical Flow Guidance—0
Task-Oriented Semantic Communication for Stereo-Vision 3D Object Detection—0
L4P: Low-Level 4D Vision Perception Unified—0
Robust 6DoF Pose Tracking Considering Contour and Interior Correspondence Uncertainty for AR Assembly Guidance—0
Learning semantical dynamics and spatiotemporal collaboration for human pose estimation in video—0
Super Resolution image reconstructs via total variation-based image deconvolution: a majorization-minimization approach—0
FloVD: Optical Flow Meets Video Diffusion Model for Enhanced Camera-Controlled Video Synthesis—0
VidCRAFT3: Camera, Object, and Lighting Control for Image-to-Video Generation—0
A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision—0
Flow Distillation Sampling: Regularizing 3D Gaussians with Pre-trained Matching Priors—0
A Physical Coherence Benchmark for Evaluating Video Generation Models via Optical Flow-guided Frame PredictionCode0
MotionAgent: Fine-grained Controllable Video Generation via Motion Field Agent—0
XR-VIO: High-precision Visual Inertial Odometry with Fast Initialization for XR Applications—0
REMOTE: Real-time Ego-motion Tracking for Various Endoscopes via Multimodal Visual Feature Learning—0
Extending Information Bottleneck Attribution to Video SequencesCode0
Improved Encoding for Overfitted Video Codecs—0
Image Velocimetry using Direct Displacement Field estimation with Neural Networks for FluidsCode0
GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection—0
Machine Learning Modeling for Multi-order Human Visual Motion ProcessingCode0
MONA: Moving Object Detection from Videos Shot by Dynamic Camera—0
VipDiff: Towards Coherent and Diverse Video Inpainting via Training-free Denoising Diffusion Models—0
Learning segmentation from point trajectories—0
Event-based vision for egomotion estimation using precise event timing—0
Multi-modal Fusion and Query Refinement Network for Video Moment Retrieval and Highlight Detection—0
Quadcopter Position Hold Function using Optical Flow in a Smartphone-based Flight Computer—0
VanGogh: A Unified Multimodal Diffusion-based Framework for Video Colorization—0
Edit as You See: Image-guided Video Editing via Masked Motion Modeling—0
ProTracker: Probabilistic Integration for Robust and Accurate Point Tracking—0
AHMSA-Net: Adaptive Hierarchical Multi-Scale Attention Network for Micro-Expression Recognition—0
Shape and Texture: What Influences Reliable Optical Flow Estimation?—0
Explicit Depth-Aware Blurry Video Frame Interpolation Guided by Differential Curves—0
BiM-VFI: Bidirectional Motion Field-Guided Frame Interpolation for Video with Non-uniform Motions—0
Graph Neural Network Combining Event Stream and Periodic Aggregation for Low-Latency Event-based Vision—0
Hierarchical Flow Diffusion for Efficient Frame Interpolation—0
HuPerFlow: A Comprehensive Benchmark for Human vs. Machine Motion Estimation Comparison—0
Spatially-guided Temporal Aggregation for Robust Event-RGB Optical Flow Estimation—0
Multi-Modal Synergistic Implicit Image Enhancement for Efficient Optical Flow Estimation—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
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