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 901–950 of 2184 papers

TitleStatusHype
Freespace Optical Flow Modeling for Automated Driving—0
A Memory-Augmented Multi-Task Collaborative Framework for Unsupervised Traffic Accident Detection in Driving Videos—0
Revisiting Event-based Video Frame Interpolation—0
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
Video Frame Interpolation with Stereo Event and Intensity Camera—0
Close-up View synthesis by Interpolating Optical Flow—0
Unsupervised Optical Flow Estimation with Dynamic Timing Representation for Spike Camera—0
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
S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural NetworksCode0
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
Neural World Models for Computer Vision—0
1st Solution Places for CVPR 2023 UG^2+ Challenge Track 2.1-Text Recognition through Atmospheric TurbulenceCode0
Unmasking Deepfakes: Masked Autoencoding Spatiotemporal Transformers for Enhanced Video Forgery Detection—0
DIFT: Dynamic Iterative Field Transforms for Memory Efficient Optical Flow—0
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
Unifying (Machine) Vision via Counterfactual World Modeling—0
The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation—0
A Multi-Modal Transformer Network for Action Detection—0
FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses via Pixel-Aligned Scene Flow—0
Video ControlNet: Towards Temporally Consistent Synthetic-to-Real Video Translation Using Conditional Image Diffusion Models—0
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
Parallelizing Optical Flow Estimation on an Ultra-Low Power RISC-V Cluster for Nano-UAV Navigation—0
FEDORA: Flying Event Dataset fOr Reactive behAvior—0
A Fast and Accurate Optical Flow Camera for Resource-Constrained Edge Applications—0
SCP: Soft Conditional Prompt Learning for Aerial Video Action Recognition—0
SIDAR: Synthetic Image Dataset for Alignment & RestorationCode0
IDO-VFI: Identifying Dynamics via Optical Flow Guidance for Video Frame Interpolation with EventsCode0
S^3Track: Self-supervised Tracking with Soft Assignment Flow—0
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
Can SAM Boost Video Super-Resolution?—0
Learning Dynamic Point Cloud Compression via Hierarchical Inter-frame Block MatchingCode0
RMES: Real-Time Micro-Expression Spotting Using Phase From Riesz PyramidCode0
Weakly-supervised Micro- and Macro-expression Spotting Based on Multi-level Consistency—0
SSTM: Spatiotemporal Recurrent Transformers for Multi-frame Optical Flow EstimationCode0
Rip Current Detection in Nearshore Areas through UAV Video Analysis with Almost Local-Isometric Embedding Techniques on Sphere—0
TransFlow: Transformer as Flow Learner—0
FSNet: Redesign Self-Supervised MonoDepth for Full-Scale Depth Prediction for Autonomous Driving—0
LLA-FLOW: A Lightweight Local Aggregation on Cost Volume for Optical Flow Estimation—0
Neuromorphic Optical Flow and Real-time Implementation with Event Cameras—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
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