{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:PA3YYVBDNQLS7CQHE6F2MAOUAT","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ff165ae3318969c5ca99aff489c844b93b1627b57c98441e887d78914cd33cdd","cross_cats_sorted":["cs.AI","cs.CY","cs.LG","cs.MM"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.HC","submitted_at":"2022-10-20T21:28:34Z","title_canon_sha256":"d24cf011a488330fd5e9832d28e40b3453bc8c0a2dcfa908dc9cb997ec0ce01a"},"schema_version":"1.0","source":{"id":"2210.11603","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.11603","created_at":"2026-07-05T06:36:10Z"},{"alias_kind":"arxiv_version","alias_value":"2210.11603v2","created_at":"2026-07-05T06:36:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.11603","created_at":"2026-07-05T06:36:10Z"},{"alias_kind":"pith_short_12","alias_value":"PA3YYVBDNQLS","created_at":"2026-07-05T06:36:10Z"},{"alias_kind":"pith_short_16","alias_value":"PA3YYVBDNQLS7CQH","created_at":"2026-07-05T06:36:10Z"},{"alias_kind":"pith_short_8","alias_value":"PA3YYVBD","created_at":"2026-07-05T06:36:10Z"}],"graph_snapshots":[{"event_id":"sha256:f93d8972f7d09c2526d94e6f0b48f143311edc72f52c9535e5dba38f23bee96b","target":"graph","created_at":"2026-07-05T06:36:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2210.11603/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Text-to-image AI are capable of generating novel images for inspiration, but their applications for 3D design workflows and how designers can build 3D models using AI-provided inspiration have not yet been explored. To investigate this, we integrated DALL-E, GPT-3, and CLIP within a CAD software in 3DALL-E, a plugin that generates 2D image inspiration for 3D design. 3DALL-E allows users to construct text and image prompts based on what they are modeling. In a study with 13 designers, we found that designers saw great potential in 3DALL-E within their workflows and could use text-to-image AI to","authors_text":"George Fitzmaurice, Jo Vermeulen, Justin Matejka, Vivian Liu","cross_cats":["cs.AI","cs.CY","cs.LG","cs.MM"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.HC","submitted_at":"2022-10-20T21:28:34Z","title":"3DALL-E: Integrating Text-to-Image AI in 3D Design Workflows"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.11603","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:041783887de5d98cf9bf994986329509c5d7c6da9493129985f1ad71c712b942","target":"record","created_at":"2026-07-05T06:36:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ff165ae3318969c5ca99aff489c844b93b1627b57c98441e887d78914cd33cdd","cross_cats_sorted":["cs.AI","cs.CY","cs.LG","cs.MM"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.HC","submitted_at":"2022-10-20T21:28:34Z","title_canon_sha256":"d24cf011a488330fd5e9832d28e40b3453bc8c0a2dcfa908dc9cb997ec0ce01a"},"schema_version":"1.0","source":{"id":"2210.11603","kind":"arxiv","version":2}},"canonical_sha256":"78378c54236c172f8a07278ba601d404c578936a19531715a1f68ef257ba76a1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"78378c54236c172f8a07278ba601d404c578936a19531715a1f68ef257ba76a1","first_computed_at":"2026-07-05T06:36:10.664984Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:36:10.664984Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bBwyyd2r3h4lF437eY/zZ47EyS1FGQAtNknfYQ+rfKwzOECE1SGYqzf9UETdgkWThDo62peagafEM4zDWzidAA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:36:10.665587Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.11603","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:041783887de5d98cf9bf994986329509c5d7c6da9493129985f1ad71c712b942","sha256:f93d8972f7d09c2526d94e6f0b48f143311edc72f52c9535e5dba38f23bee96b"],"state_sha256":"e980b3964da4d68330c275a4dc9cea4ff833d5d3a6d7743d76b5d448ba4912e5"}