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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.","url_abs":"https://arxiv.org/abs/2406.09754v2","url_pdf":"https://arxiv.org/pdf/2406.09754v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"lavib-a-large-scale-video-interpolation","repo_url":"https://github.com/alexandrosstergiou/lavib","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"video-frame-interpolation","task_name":"Video Frame Interpolation"}],"methods":[],"datasets_introduced":[{"slug":"lavib","name":"LAVIB","full_name":"Large-scale Video Interpolation Benchmark"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-frame-interpolation-on-lavib","task":"Video Frame Interpolation","dataset":"LAVIB","model":"FLAVR","rank_in_archive_order":1,"of":3,"metrics":{"LPIPS":"0.02934","PSNR":"33.44","SSIM":"0.981"},"uses_additional_data":false},{"leaderboard":"/sota/video-frame-interpolation-on-lavib","task":"Video Frame Interpolation","dataset":"LAVIB","model":"EMA-VFI","rank_in_archive_order":2,"of":3,"metrics":{"LPIPS":"0.03105","PSNR":"33.14","SSIM":"0.978"},"uses_additional_data":false},{"leaderboard":"/sota/video-frame-interpolation-on-lavib","task":"Video Frame Interpolation","dataset":"LAVIB","model":"RIFE","rank_in_archive_order":3,"of":3,"metrics":{"LPIPS":"0.1416","PSNR":"27.88","SSIM":"0.871"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2406.09754","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.09754"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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