{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/text-to-3d/papers/4","list_of":"/task/text-to-3d","task":"Text to 3D","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":4,"pages_in_order":4,"rows_per_page":100,"rows":[301,314],"of":314,"counts":{"archive_papers_tagged":314,"with_a_code_link":102,"where_syntology_ran_a_sample":56,"not_listed_spam_title":0,"listed":314,"listed_where_code_ran":56,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":49,"every_run_a_failure_of_syntologys_instrument":7,"listed_with_a_run_with_no_instrument_failure":49,"listed_every_run_a_failure_of_syntologys_instrument":7,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/text-to-3d","prev":"/task/text-to-3d/papers/3","next":null,"papers":[{"url":"/paper/monocular-depth-estimation-using-diffusion","slug":"monocular-depth-estimation-using-diffusion","title":"Monocular Depth Estimation using Diffusion Models","date":"2023-02-28","arxiv_id":"2302.14816","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-driven-visual-synthesis-with-latent","title":"Text-driven Visual Synthesis with Latent Diffusion Prior","date":"2023-02-16","arxiv_id":"2302.08510","repositories_listed":0,"syntology":null},{"url":null,"slug":"chatgpt-is-not-all-you-need-a-state-of-the","title":"ChatGPT is not all you need. A State of the Art Review of large Generative AI models","date":"2023-01-11","arxiv_id":"2301.04655","repositories_listed":0,"syntology":null},{"url":null,"slug":"dream3d-zero-shot-text-to-3d-synthesis-using","title":"Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Models","date":"2022-12-28","arxiv_id":"2212.14704","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-ldm-neural-implicit-3d-shape-generation","title":"3D-LDM: Neural Implicit 3D Shape Generation with Latent Diffusion Models","date":"2022-12-01","arxiv_id":"2212.00842","repositories_listed":0,"syntology":null},{"url":null,"slug":"vectorfusion-text-to-svg-by-abstracting-pixel","title":"VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models","date":"2022-11-21","arxiv_id":"2211.11319","repositories_listed":0,"syntology":null},{"url":null,"slug":"textcraft-zero-shot-generation-of-high","title":"CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Natural Language","date":"2022-11-02","arxiv_id":"2211.01427","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-pure-clip-guidance-for-voxel","title":"Understanding Pure CLIP Guidance for Voxel Grid NeRF Models","date":"2022-09-30","arxiv_id":"2209.15172","repositories_listed":0,"syntology":null},{"url":"/paper/0-1-deep-neural-networks-via-block-coordinate","slug":"0-1-deep-neural-networks-via-block-coordinate","title":"0/1 Deep Neural Networks via Block Coordinate Descent","date":"2022-06-19","arxiv_id":"2206.09379","repositories_listed":0,"syntology":null},{"url":null,"slug":"sceneseer-3d-scene-design-with-natural","title":"SceneSeer: 3D Scene Design with Natural Language","date":"2017-02-28","arxiv_id":"1703.00050","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-to-3d-scene-generation-with-rich-lexical","title":"Text to 3D Scene Generation with Rich Lexical Grounding","date":"2015-05-23","arxiv_id":"1505.06289","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-spatial-knowledge-for-text-to-3d","title":"Learning Spatial Knowledge for Text to 3D Scene Generation","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-learning-of-spatial-knowledge-for","title":"Interactive Learning of Spatial Knowledge for Text to 3D Scene Generation","date":"2014-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-parsing-for-text-to-3d-scene","title":"Semantic Parsing for Text to 3D Scene Generation","date":"2014-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"8af734bee5a254817bf40f6dd33383f84a06a41f1e37306f1001a626e123626e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}