{"url":"/dataset/cstomstudio","name":"CstomStudio","full_name":null,"description_markdown":"- This dataset is composed of 69 individual objects and 57 meaningful pairs. \r\n- The objects cover a wide range of categories, including decor item, food, furniture, instrument, jewelry, luggage, person, pet, plant, plushie, scene, thing, toy, transportation, and wearable item.","description_withheld":null,"homepage":"https://kyfafyd.wang/projects/customvideo","introduced_date":"2024-01-18","introduced_date_note":null,"introduced_by":{"paper":"/paper/customvideo-customizing-text-to-video","title":"CustomVideo: Customizing Text-to-Video Generation with Multiple Subjects","first_author":"Zhao Wang","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["CstomStudio"],"data_loaders":[],"num_papers_in_archive":1,"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."}