{"url":"/dataset/diffusion4d","name":"Diffusion4D","full_name":null,"description_markdown":"Diffusion4D is a large-scale, high-quality dynamic 3D(4D) dataset sourced from the vast 3D data corpus of Objaverse-1.0 and Objaverse-XL. We apply a series of empirical rules to filter the dataset. You can find more details in our paper. In this part, we will release the selected 4D assets, including:\r\n\r\n1. Selected high-quality 4D object ID.\r\n2. A render script using Blender, providing optional settings to render your personalized data.\r\n3. Rendered 4D images by our team to save your GPU time. With 8 GPUs and a total of 16 threads, it took 5.5 days to render the curated objaverse-1.0 dataset.","description_withheld":null,"homepage":"https://huggingface.co/datasets/hw-liang/Diffusion4D","introduced_date":"2024-05-26","introduced_date_note":null,"introduced_by":{"paper":"/paper/diffusion4d-fast-spatial-temporal-consistent","title":"Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models","first_author":"Hanwen Liang","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["Diffusion4D"],"data_loaders":[],"num_papers_in_archive":3,"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."}