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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 151–200 of 2184 papers

TitleStatusHype
Enhancing Marine Debris Acoustic Monitoring by Optical Flow-Based Motion Vector Analysis—0
Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark—0
Generalized Uncertainty-Based Evidential Fusion with Hybrid Multi-Head Attention for Weak-Supervised Temporal Action LocalizationCode0
Leveraging Consistent Spatio-Temporal Correspondence for Robust Visual OdometryCode0
SOUS VIDE: Cooking Visual Drone Navigation Policies in a Gaussian Splatting Vacuum—0
MotiF: Making Text Count in Image Animation with Motion Focal Loss—0
Dynamic semantic VSLAM with known and unknown objects—0
SurgSora: Object-Aware Diffusion Model for Controllable Surgical Video Generation—0
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices—0
GG-SSMs: Graph-Generating State Space Models—0
BiM-VFI: directional Motion Field-Guided Frame Interpolation for Video with Non-uniform MotionsCode2
Spatiotemporal Blind-Spot Network with Calibrated Flow Alignment for Self-Supervised Video DenoisingCode1
Exploring More from Multiple Gait Modalities for Human Identification—0
Learning Normal Flow Directly From Event NeighborhoodsCode1
ResFlow: Fine-tuning Residual Optical Flow for Event-based High Temporal Resolution Motion Estimation—0
Mojito: Motion Trajectory and Intensity Control for Video Generation—0
eCARLA-scenes: A synthetically generated dataset for event-based optical flow predictionCode0
Labits: Layered Bidirectional Time Surfaces Representation for Event Camera-based Continuous Dense Trajectory Estimation—0
A Plug-and-Play Algorithm for 3D Video Super-Resolution of Single-Photon LiDAR data—0
Static-Dynamic Class-level Perception Consistency in Video Semantic Segmentation—0
EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based VisionCode1
Local Attention Transformers for High-Detail Optical Flow Upsampling—0
MotionStone: Decoupled Motion Intensity Modulation with Diffusion Transformer for Image-to-Video Generation—0
Deep Learning and Hybrid Approaches for Dynamic Scene Analysis, Object Detection and Motion Tracking—0
Advancing Auto-Regressive Continuation for Video Frames—0
Improving Dynamic Object Interactions in Text-to-Video Generation with AI Feedback—0
STATIC : Surface Temporal Affine for TIme Consistency in Video Monocular Depth Estimation—0
Advanced Video Inpainting Using Optical Flow-Guided Efficient DiffusionCode3
Hybrid Local-Global Context Learning for Neural Video Compression—0
RoMo: Robust Motion Segmentation Improves Structure from Motion—0
An End-to-End Two-Stream Network Based on RGB Flow and Representation Flow for Human Action Recognition—0
Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors—0
Context-Aware Input Orchestration for Video Inpainting—0
Optical-Flow Guided Prompt Optimization for Coherent Video Generation—0
TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks—0
Benchmarking the Robustness of Optical Flow Estimation to CorruptionsCode0
PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation—0
Efficient Masked AutoEncoder for Video Object Counting and A Large-Scale Benchmark—0
DATAP-SfM: Dynamic-Aware Tracking Any Point for Robust Structure from Motion in the Wild—0
OnlyFlow: Optical Flow based Motion Conditioning for Video Diffusion ModelsCode1
RenderBender: A Survey on Adversarial Attacks Using Differentiable Rendering—0
MFTIQ: Multi-Flow Tracker with Independent Matching Quality EstimationCode1
DPU: Dynamic Prototype Updating for Multimodal Out-of-Distribution DetectionCode2
Scaling Properties of Diffusion Models for Perceptual Tasks—0
Breaking The Ice: Video Segmentation for Close-Range Ice-Covered Waters—0
AMNCutter: Affinity-Attention-Guided Multi-View Normalized Cutter for Unsupervised Surgical Instrument SegmentationCode0
Object segmentation from common fate: Motion energy processing enables human-like zero-shot generalization to random dot stimuliCode0
Optical Flow Representation Alignment Mamba Diffusion Model for Medical Video Generation—0
Optimizing Violence Detection in Video Classification Accuracy through 3D Convolutional Neural Networks—0
Motion Graph Unleashed: A Novel Approach to Video PredictionCode0
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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