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Computer Science > Computer Vision and Pattern Recognition

arXiv:2406.09754 (cs)
[Submitted on 14 Jun 2024 (v1), last revised 20 Oct 2024 (this version, v2)]

Title:LAVIB: A Large-scale Video Interpolation Benchmark

Authors:Alexandros Stergiou
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Abstract:This paper introduces a LArge-scale Video Interpolation Benchmark (LAVIB) for the low-level video task of Video Frame Interpolation (VFI). LAVIB comprises a large collection of high-resolution videos sourced from the web through an automated pipeline with minimal requirements for human verification. Metrics are computed for each video's motion magnitudes, luminance conditions, frame sharpness, and contrast. The collection of videos and the creation of quantitative challenges based on these metrics are under-explored by current low-level video task datasets. In total, LAVIB includes 283K clips from 17K ultra-HD videos, covering 77.6 hours. Benchmark train, val, and test sets maintain similar video metric distributions. Further splits are also created for out-of-distribution (OOD) challenges, with train and test splits including videos of dissimilar attributes.
Comments: Accepted at NeurIPS 2024 Datasets and Benchmarks Track, project page: this https URL
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2406.09754 [cs.CV]
  (or arXiv:2406.09754v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2406.09754

Submission history

From: Alexandros Stergiou [view email]
[v1] Fri, 14 Jun 2024 06:44:01 UTC (17,248 KB)
[v2] Sun, 20 Oct 2024 09:44:30 UTC (31,213 KB)
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