{"about":{"site":"https://codewithpapers.app","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.","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"},"url":"/paper/text2shape-generating-shapes-from-natural","title":"Text2Shape: Generating Shapes from Natural Language by Learning Joint Embeddings","arxiv_id":"1803.08495","date":"2018-03-22","proceeding":null,"authors":["Kevin Chen","Christopher B. Choy","Manolis Savva","Angel X. Chang","Thomas Funkhouser","Silvio Savarese"],"abstract":"We present a method for generating colored 3D shapes from natural language.\nTo this end, we first learn joint embeddings of freeform text descriptions and\ncolored 3D shapes. Our model combines and extends learning by association and\nmetric learning approaches to learn implicit cross-modal connections, and\nproduces a joint representation that captures the many-to-many relations\nbetween language and physical properties of 3D shapes such as color and shape.\nTo evaluate our approach, we collect a large dataset of natural language\ndescriptions for physical 3D objects in the ShapeNet dataset. With this learned\njoint embedding we demonstrate text-to-shape retrieval that outperforms\nbaseline approaches. Using our embeddings with a novel conditional Wasserstein\nGAN framework, we generate colored 3D shapes from text. Our method is the first\nto connect natural language text with realistic 3D objects exhibiting rich\nvariations in color, texture, and shape detail. See video at\nhttps://youtu.be/zraPvRdl13Q","url_abs":"http://arxiv.org/abs/1803.08495v1","url_pdf":"http://arxiv.org/pdf/1803.08495v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"text2shape-generating-shapes-from-natural","repo_url":"https://github.com/kchen92/text2shape","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"text2shape-generating-shapes-from-natural","repo_url":"https://github.com/maxim0815/text2shape","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"metric-learning","task_name":"Metric Learning"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1803.08495","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.08495"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/kchen92/text2shape","reach":{"status":"ok","spdx":"NOASSERTION"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/maxim0815/text2shape","reach":null}],"summary":{"ran_honours":1},"by_repo_kind":{"listed":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"ecf944473b4c3dde","entry":"find_positive_shape_id","repo":"maxim0815/text2shape","repo_kind":"listed","path":"t-SNE.py","file_url":"https://github.com/maxim0815/text2shape/blob/HEAD/t-SNE.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"ecf944473b4c3dde"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}