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Learned optical flow estimation

Estimates optical flow with multi-frame models, occlusion awareness and graph reasoning.

73 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026) · an approach in Video segmentation and tracking, keyword cluster “interpolation · flow · event”

State History Benchmarks Tried Checklist Preprints Who Export

Where it stands

Few recent reports None of the tracked benchmarks has a report from 2025–26.

15 papers in 2023–24 and 20 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic held steady, from 3.4% to 3.7%.

No longer reported by papers here: GOPRO (PSNR, last in 2024); GOPRO (SSIM, last in 2024).

How this is decided

A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.

How it got here

2016: 1162017: 6172018: 5182019: 7192020: 7202021: 5212022: 7222023: 5232024: 10242025: 9252026: 1126

Most cited paper of each year

Earliest papers here

Benchmark records

The best number each year that beat every earlier one, among papers of this approach reporting their own method. Numbers are extracted by AI from each paper's text; check the paper before citing.

GOPRO · PSNR (higher is better)

5 papers, 2020–2024 · not reported since 2024

2020: 31.672022: 33.322024: 35.98
20202024

GOPRO · SSIM (higher is better)

5 papers, 2020–2024 · not reported since 2024

2020: 0.9282022: 0.9632024: 0.979
20202024

What has been tried

60 papers here report 625 measured changes (a component added, removed or swapped, compared with a baseline): helped 74%, no clear effect 5%, hurt 18%, mixed 3%. Papers mostly publish changes that helped, so "helped" is over-represented.

Ablations in recent papers

What was changedMeasured onResultEffectPaper
remove 2-frame pretrainingSintel clean · EPE1.00 -> 1.16HurtMemFlow: Optical Flow Estimation and Prediction with Memory (CVPR 2024 · 45 citations)
add track visibility informationCVO Final · EPE (all)1.46 -> 1.43HelpedDense Optical Tracking: Connecting the Dots (CVPR 2024 · 30 citations)
remove BBFPDVD · PSNR34.95 -> 34.74HurtBlur-aware Spatio-temporal Sparse Transformer for Video Deblurring (CVPR 2024 · 25 citations)
Add the dual-pyramid encoderMPI-Sintel final · EPE2.58 -> 2.51HelpedDPFlow: Adaptive Optical Flow Estimation with a Dual-Pyramid Framework (CVPR 2025 · 15 citations)
add motion prior from optical-flow trajectoriesTAP-Vid DAVIS · AJ60.5 -> 63.2HelpedFlowTrack: Revisiting Optical Flow for Long-Range Dense Tracking (CVPR 2024 · 13 citations)
add the self-corrector branch to CDCDSEC · EPE0.89 -> 0.81HelpedEfficient Meshflow and Optical Flow Estimation from Event Cameras (CVPR 2024 · 12 citations)
replace depth weighting with occlusion-aware weightingKITTI VFI · SSIM0.733 -> 0.734No clear effectOCAI: Improving Optical Flow Estimation by Occlusion and Consistency Aware Interpolation (CVPR 2024 · 12 citations)
Use BOFC instead of temporal-frame replacement or movement-speed correctionDNV real-world · NS (%)91.48; 91.88 -> 93.19HelpedDanceFix: An Exploration in Group Dance Neatness Assessment Through Fixing Abnormal Challenges of Human Pose (AAAI 2025 · 11 citations)
add video stabilizationCLEAR dataset · PSNR26.80 to 28.09HelpedTurb-Seg-Res: A Segment-then-Restore Pipeline for Dynamic Videos with Atmospheric Turbulence (CVPR 2024 · 11 citations)
Use the SIM pipeline instead of the recursive pipelineSintel · runtime (ms)122.18 -> 84.59HelpedStreamFlow: Streamlined Multi-Frame Optical Flow Estimation for Video Sequences (NeurIPS 2024 · 5 citations)
add Motion Information Propagation (MIP)KITTI-2015 train · F1-all8.19 -> 7.12HelpedM2Flow: A Motion Information Fusion Framework for Enhanced Unsupervised Optical Flow Estimation in Autonomous Driving (AAAI 2025 · 2 citations)
add difference motion encodingDSEC test · EPE0.82 -> 0.72HelpedEDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation (CVPR 2025 · 2 citations)

What reviewers will expect

Datasets and baselines named in the 12 papers of this approach from 2025–2026 that have a full-text summary. Leaving out the common ones is a frequent review question.

Datasets

  • KITTI 2015 · 3 of 12 papers
  • Sintel · 2 of 12 papers
  • FlyingChairs · 2 of 12 papers
  • DSEC · 2 of 12 papers

Baselines

  • RAFT · 2 of 12 papers
  • FlowFormer++ · 2 of 12 papers
  • FlowDiffuser · 2 of 12 papers
  • GMA · 2 of 12 papers
  • E-RAFT · 2 of 12 papers

Recent preprints

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    Who publishes on it

    Institutions on the most papers of this approach since 2023 (of 35 papers; from OpenAlex and the papers' affiliations).

    Use it

    Download BibTeX of 40 key papersOpen the topic in the interactive map

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    Same keyword cluster

    Elsewhere in the topic

    Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).

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