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 1–50 of 2184 papers

TitleStatusHype
Video Depth Anything: Consistent Depth Estimation for Super-Long VideosCode5
Go-with-the-Flow: Motion-Controllable Video Diffusion Models Using Real-Time Warped NoiseCode5
DepthCrafter: Generating Consistent Long Depth Sequences for Open-world VideosCode5
Infinite Photorealistic Worlds using Procedural GenerationCode5
ProPainter: Improving Propagation and Transformer for Video InpaintingCode5
RAPIDFlow: Recurrent Adaptable Pyramids with Iterative Decoding for Efficient Optical Flow EstimationCode4
Easi3R: Estimating Disentangled Motion from DUSt3R Without TrainingCode4
DPFlow: Adaptive Optical Flow Estimation with a Dual-Pyramid FrameworkCode4
FlowMap: High-Quality Camera Poses, Intrinsics, and Depth via Gradient DescentCode4
VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play Context ControlCode4
Kubric: A scalable dataset generatorCode4
Tracking Everything Everywhere All at OnceCode4
SEA-RAFT: Simple, Efficient, Accurate RAFT for Optical FlowCode4
FILM: Frame Interpolation for Large MotionCode4
CoTracker: It is Better to Track TogetherCode4
Thin-Plate Spline Motion Model for Image AnimationCode4
Recurrent Partial Kernel Network for Efficient Optical Flow EstimationCode4
DiffuEraser: A Diffusion Model for Video InpaintingCode4
Advanced Video Inpainting Using Optical Flow-Guided Efficient DiffusionCode3
FruitNeRF: A Unified Neural Radiance Field based Fruit Counting FrameworkCode3
Event-Enhanced Blurry Video Super-ResolutionCode3
Unifying Flow, Stereo and Depth EstimationCode3
DreamScene4D: Dynamic Multi-Object Scene Generation from Monocular VideosCode3
Towards An End-to-End Framework for Flow-Guided Video InpaintingCode3
VideoCutLER: Surprisingly Simple Unsupervised Video Instance SegmentationCode3
Stonefish: Supporting Machine Learning Research in Marine RoboticsCode3
TAP-Vid: A Benchmark for Tracking Any Point in a VideoCode3
NGD-SLAM: Towards Real-Time Dynamic SLAM without GPUCode3
DBA-Fusion: Tightly Integrating Deep Dense Visual Bundle Adjustment with Multiple Sensors for Large-Scale Localization and MappingCode3
Moving Object Segmentation: All You Need Is SAM (and Flow)Code3
NeuFlow v2: High-Efficiency Optical Flow Estimation on Edge DevicesCode3
LISO: Lidar-only Self-Supervised 3D Object DetectionCode2
Learning to See Through ObstructionsCode2
Mask-Free Video Instance SegmentationCode2
InstMove: Instance Motion for Object-centric Video SegmentationCode2
Learning Optical Flow and Scene Flow with Bidirectional Camera-LiDAR FusionCode2
MEMFOF: High-Resolution Training for Memory-Efficient Multi-Frame Optical Flow EstimationCode2
IFRNet: Intermediate Feature Refine Network for Efficient Frame InterpolationCode2
CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical FlowCode2
Free-SurGS: SfM-Free 3D Gaussian Splatting for Surgical Scene ReconstructionCode2
Flow-Guided Diffusion for Video InpaintingCode2
Breaking of brightness consistency in optical flow with a lightweight CNN networkCode2
GMFlow: Learning Optical Flow via Global MatchingCode2
MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian SplattingCode2
BiM-VFI: directional Motion Field-Guided Frame Interpolation for Video with Non-uniform MotionsCode2
Exploiting Optical Flow Guidance for Transformer-Based Video InpaintingCode2
EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-SupervisionCode2
Blur-aware Spatio-temporal Sparse Transformer for Video DeblurringCode2
FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editingCode2
AI-Generated Video Detection via Spatio-Temporal Anomaly LearningCode2
Show:102550
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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