{"url":"/dataset/animerun","name":"AnimeRun","full_name":null,"description_markdown":"**AnimeRun** is a 2D animation visual correspondence dataset. It is designed for tasks converting open source three-dimensional (3D) movies to full scenes in 2D style, including simultaneous moving background and interactions of multiple subjects.\r\n\r\nSource: [AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies](https://arxiv.org/pdf/2211.05709v1.pdf)\r\n\r\nImage Source: [https://arxiv.org/pdf/2211.05709v1.pdf](https://arxiv.org/pdf/2211.05709v1.pdf)","description_withheld":null,"homepage":"https://lisiyao21.github.io/projects/AnimeRun","introduced_date":"2022-11-10","introduced_date_note":null,"introduced_by":{"paper":"/paper/animerun-2d-animation-visual-correspondence","title":"AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies","first_author":"Li SiYao","url":null},"license":{"name":"Creative Common CC-BY 4.0","url":"https://creativecommons.org/licenses/by-nc/4.0/"},"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[],"languages":[],"variants":["AnimeRun"],"data_loaders":[],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}