{"url":"/dataset/t-3-bench","name":"T$^3$Bench","full_name":null,"description_markdown":"T$^3$Bench is the first comprehensive text-to-3D benchmark containing diverse text prompts of three increasing complexity levels that are specially designed for 3D generation (300 prompts in total). To assess both the subjective quality and the text alignment, we propose two automatic metrics based on multi-view images produced by the 3D contents. The quality metric combines multi-view text-image scores and regional convolution to detect quality and view inconsistency. The alignment metric uses multi-view captioning and Large Language Model (LLM) evaluation to measure text-3D consistency.","description_withheld":null,"homepage":"https://t3bench.com/","introduced_date":"2023-10-04","introduced_date_note":null,"introduced_by":{"paper":"/paper/t-3-bench-benchmarking-current-progress-in","title":"T$^3$Bench: Benchmarking Current Progress in Text-to-3D Generation","first_author":"Yuze He","url":null},"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"},{"name":"3D","url":"/datasets/modality/3d"}],"tasks":[{"name":"Text to 3D","url":"/task/text-to-3d","datasets_with_task":"/datasets/task/text-to-3d"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["T$^3$Bench"],"data_loaders":[{"repo":"https://github.com/THU-LYJ-Lab/T3Bench","url":"https://github.com/THU-LYJ-Lab/T3Bench","frameworks":["pytorch"]}],"num_papers_in_archive":8,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/text-to-3d-on-t-3-bench","task":"Text to 3D","dataset_variant":"T$^3$Bench","rows":6,"metrics":["Avg"],"first_row_in_archive_order":{"model":"ProlificDreamer","paper":"/paper/0-1-deep-neural-networks-via-block-coordinate","metrics":{"Avg":"43.3"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/fantasia3d-disentangling-geometry-and","title":"Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation","date":"2023-03-24","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":18,"samples_ran":7,"samples_unverified":11,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/score-jacobian-chaining-lifting-pretrained-2d","title":"Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation","date":"2022-12-01","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/magic3d-high-resolution-text-to-3d-content","title":"Magic3D: High-Resolution Text-to-3D Content Creation","date":"2022-11-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/latent-nerf-for-shape-guided-generation-of-3d","title":"Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures","date":"2022-11-14","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":2,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dreamfusion-text-to-3d-using-2d-diffusion","title":"DreamFusion: Text-to-3D using 2D Diffusion","date":"2022-09-29","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":10,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/0-1-deep-neural-networks-via-block-coordinate","title":"0/1 Deep Neural Networks via Block Coordinate Descent","date":"2022-06-19","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":36,"samples_ran":22,"samples_unverified":14,"pointer_only_for_licence":3,"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."}