{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:XSO746AHTSV3FGGJQI2EAQDWCF","short_pith_number":"pith:XSO746AH","schema_version":"1.0","canonical_sha256":"bc9dfe78079cabb298c98234404076114ba04cd009efc6086b6291fe7d8ba71e","source":{"kind":"arxiv","id":"1808.08871","version":2},"attestation_state":"computed","paper":{"title":"B\\'ezierGAN: Automatic Generation of Smooth Curves from Interpretable Low-Dimensional Parameters","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CG","stat.ML"],"primary_cat":"cs.LG","authors_text":"Mark Fuge, Wei Chen","submitted_at":"2018-08-27T14:57:17Z","abstract_excerpt":"Many real-world objects are designed by smooth curves, especially in the domain of aerospace and ship, where aerodynamic shapes (e.g., airfoils) and hydrodynamic shapes (e.g., hulls) are designed. To facilitate the design process of those objects, we propose a deep learning based generative model that can synthesize smooth curves. The model maps a low-dimensional latent representation to a sequence of discrete points sampled from a rational B\\'ezier curve. We demonstrate the performance of our method in completing both synthetic and real-world generative tasks. Results show that our method can"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1808.08871","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-27T14:57:17Z","cross_cats_sorted":["cs.AI","cs.CG","stat.ML"],"title_canon_sha256":"456030063a9028d72ce27dd48c4b20809d63f0f40899ab7754a7c968360fea76","abstract_canon_sha256":"599939333d4ff1873bb43316e42a6b9ffb6b5f1486a7d88830669a020ef58441"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:04:25.705827Z","signature_b64":"0jW6u0pVG2h7ZQU6+7q2We7YgCcBf6OA/Sovg1/DvreS8aqWNlHmQpaWPpVj41SGOrT3kp2zqeYE6VUKK7qoCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc9dfe78079cabb298c98234404076114ba04cd009efc6086b6291fe7d8ba71e","last_reissued_at":"2026-07-05T02:04:25.705329Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:04:25.705329Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"B\\'ezierGAN: Automatic Generation of Smooth Curves from Interpretable Low-Dimensional Parameters","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CG","stat.ML"],"primary_cat":"cs.LG","authors_text":"Mark Fuge, Wei Chen","submitted_at":"2018-08-27T14:57:17Z","abstract_excerpt":"Many real-world objects are designed by smooth curves, especially in the domain of aerospace and ship, where aerodynamic shapes (e.g., airfoils) and hydrodynamic shapes (e.g., hulls) are designed. To facilitate the design process of those objects, we propose a deep learning based generative model that can synthesize smooth curves. The model maps a low-dimensional latent representation to a sequence of discrete points sampled from a rational B\\'ezier curve. We demonstrate the performance of our method in completing both synthetic and real-world generative tasks. Results show that our method can"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.08871","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1808.08871/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1808.08871","created_at":"2026-07-05T02:04:25.705402+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.08871v2","created_at":"2026-07-05T02:04:25.705402+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.08871","created_at":"2026-07-05T02:04:25.705402+00:00"},{"alias_kind":"pith_short_12","alias_value":"XSO746AHTSV3","created_at":"2026-07-05T02:04:25.705402+00:00"},{"alias_kind":"pith_short_16","alias_value":"XSO746AHTSV3FGGJ","created_at":"2026-07-05T02:04:25.705402+00:00"},{"alias_kind":"pith_short_8","alias_value":"XSO746AH","created_at":"2026-07-05T02:04:25.705402+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.07141","citing_title":"SketchConcept: Sketching-based Concept Recomposition for Product Design using Generative AI","ref_index":10,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XSO746AHTSV3FGGJQI2EAQDWCF","json":"https://pith.science/pith/XSO746AHTSV3FGGJQI2EAQDWCF.json","graph_json":"https://pith.science/api/pith-number/XSO746AHTSV3FGGJQI2EAQDWCF/graph.json","events_json":"https://pith.science/api/pith-number/XSO746AHTSV3FGGJQI2EAQDWCF/events.json","paper":"https://pith.science/paper/XSO746AH"},"agent_actions":{"view_html":"https://pith.science/pith/XSO746AHTSV3FGGJQI2EAQDWCF","download_json":"https://pith.science/pith/XSO746AHTSV3FGGJQI2EAQDWCF.json","view_paper":"https://pith.science/paper/XSO746AH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.08871&json=true","fetch_graph":"https://pith.science/api/pith-number/XSO746AHTSV3FGGJQI2EAQDWCF/graph.json","fetch_events":"https://pith.science/api/pith-number/XSO746AHTSV3FGGJQI2EAQDWCF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XSO746AHTSV3FGGJQI2EAQDWCF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XSO746AHTSV3FGGJQI2EAQDWCF/action/storage_attestation","attest_author":"https://pith.science/pith/XSO746AHTSV3FGGJQI2EAQDWCF/action/author_attestation","sign_citation":"https://pith.science/pith/XSO746AHTSV3FGGJQI2EAQDWCF/action/citation_signature","submit_replication":"https://pith.science/pith/XSO746AHTSV3FGGJQI2EAQDWCF/action/replication_record"}},"created_at":"2026-07-05T02:04:25.705402+00:00","updated_at":"2026-07-05T02:04:25.705402+00:00"}