{"data":{"id":"10.4121/2c62e77a-6e9a-4e1b-ad73-210f274c2626","type":"dois","attributes":{"doi":"10.4121/2c62e77a-6e9a-4e1b-ad73-210f274c2626","prefix":"10.4121","suffix":"2c62e77a-6e9a-4e1b-ad73-210f274c2626","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Petter Reijalt","nameType":"Personal","givenName":"Petter","familyName":"Reijalt","nameIdentifiers":[{"nameIdentifier":"0009-0008-2628-1636","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Alexander Gielisse","givenName":"Alexander","familyName":"Gielisse","affiliation":[],"nameIdentifiers":[]},{"name":"Rickard Karlsson","nameType":"Personal","givenName":"Rickard","familyName":"Karlsson","nameIdentifiers":[{"nameIdentifier":"0009-0002-9800-0836","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Jan van Gemert","nameType":"Personal","givenName":"Jan","familyName":"van Gemert","nameIdentifiers":[{"nameIdentifier":"0000-0002-6913-0482","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]}],"titles":[{"title":"Data supporting the publication: On the Real-World Generalisability of Optical Flow Models"}],"publisher":"4TU.ResearchData","container":{},"publicationYear":2026,"subjects":[{"subject":"Artificial Intelligence and Image Processing","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0801"},{"subject":"Information and Computing Sciences","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"08"},{"subject":"Optical Flow"},{"subject":"Real-world dataset"},{"subject":"Real-world generalisability"},{"subject":"Manual annotated"},{"subject":"TAP-VID"},{"subject":"Slow Flow"},{"subject":"Time: May 2025 - March 2026"}],"contributors":[{"name":"TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems, Pattern Recognition and Bioinformatics, Computer Vision Lab","nameType":"Organizational","contributorType":"Other","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2026-08-03","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.48550/arxiv.2607.10470","relatedIdentifierType":"DOI"},{"relationType":"IsVersionOf","relatedIdentifier":"10.4121/2c62e77a-6e9a-4e1b-ad73-210f274c2626.v1","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":["image/png"],"version":"1","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This dataset supports research into the real-world generalisability of optical flow models, the computer vision task of estimating per-pixel motion correspondences between consecutive video frames. It addresses the scarcity of accurately annotated real-world optical flow data by combining three standardised sub-datasets, together comprising 8,204 frame pairs. FlowFactor (1,000 frame pairs) consists of hand-annotated correspondences collected specifically for this benchmark using a custom annotation tool. SlowFlow (3,447 frame pairs) and TAP-Flow (3,757 frame pairs) are repurposed from existing datasets, Slow Flow (Janai et al., CVPR 2017) and TAP-Vid (Doersch et al. NeurIPS 2022) respectively, restructured into a standardised, KITTI-2015-style format for consistent benchmarking. Together, these sub-datasets enable more accurate assessment of optical flow model performance under real-world conditions than existing synthetic or narrow-domain benchmarks allow.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"awardTitle":"NWO VICI 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