{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ZSAGVN2IJKT2UJYB35TX3Z5DHF","short_pith_number":"pith:ZSAGVN2I","schema_version":"1.0","canonical_sha256":"cc806ab7484aa7aa2701df677de7a3397c4b84f8ae87c98ba1692ca7dc673b56","source":{"kind":"arxiv","id":"2304.14400","version":4},"attestation_state":"computed","paper":{"title":"IconShop: Text-Guided Vector Icon Synthesis with Autoregressive Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Jing Liao, Kede Ma, Ronghuan Wu, Wanchao Su","submitted_at":"2023-04-27T17:58:02Z","abstract_excerpt":"Scalable Vector Graphics (SVG) is a popular vector image format that offers good support for interactivity and animation. Despite its appealing characteristics, creating custom SVG content can be challenging for users due to the steep learning curve required to understand SVG grammars or get familiar with professional editing software. Recent advancements in text-to-image generation have inspired researchers to explore vector graphics synthesis using either image-based methods (i.e., text -> raster image -> vector graphics) combining text-to-image generation models with image vectorization, or"},"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":"2304.14400","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-04-27T17:58:02Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"500846cef90be74a1eeb84331fbd088275e5e939b168dc6c4baba34a3791c922","abstract_canon_sha256":"05c70232826fa85159bbfafe21551a8e91554af4709aef984082f776dd2563ea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:18:21.896251Z","signature_b64":"gZJWPkoe3g4ZF5jq9CPxbf80ii6zkZ34hOmn+4+R7hWHUx+zWoE3IbrrHzZ50w551EREzJuBkEG2MzZaxLFKBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cc806ab7484aa7aa2701df677de7a3397c4b84f8ae87c98ba1692ca7dc673b56","last_reissued_at":"2026-07-05T06:18:21.895811Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:18:21.895811Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"IconShop: Text-Guided Vector Icon Synthesis with Autoregressive Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Jing Liao, Kede Ma, Ronghuan Wu, Wanchao Su","submitted_at":"2023-04-27T17:58:02Z","abstract_excerpt":"Scalable Vector Graphics (SVG) is a popular vector image format that offers good support for interactivity and animation. Despite its appealing characteristics, creating custom SVG content can be challenging for users due to the steep learning curve required to understand SVG grammars or get familiar with professional editing software. Recent advancements in text-to-image generation have inspired researchers to explore vector graphics synthesis using either image-based methods (i.e., text -> raster image -> vector graphics) combining text-to-image generation models with image vectorization, or"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.14400","kind":"arxiv","version":4},"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/2304.14400/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":"2304.14400","created_at":"2026-07-05T06:18:21.895873+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.14400v4","created_at":"2026-07-05T06:18:21.895873+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.14400","created_at":"2026-07-05T06:18:21.895873+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZSAGVN2IJKT2","created_at":"2026-07-05T06:18:21.895873+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZSAGVN2IJKT2UJYB","created_at":"2026-07-05T06:18:21.895873+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZSAGVN2I","created_at":"2026-07-05T06:18:21.895873+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2312.16476","citing_title":"SVGDreamer: Text Guided SVG Generation with Diffusion Model","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2412.10437","citing_title":"SVGFusion: A VAE-Diffusion Transformer for Vector Graphic Generation","ref_index":63,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZSAGVN2IJKT2UJYB35TX3Z5DHF","json":"https://pith.science/pith/ZSAGVN2IJKT2UJYB35TX3Z5DHF.json","graph_json":"https://pith.science/api/pith-number/ZSAGVN2IJKT2UJYB35TX3Z5DHF/graph.json","events_json":"https://pith.science/api/pith-number/ZSAGVN2IJKT2UJYB35TX3Z5DHF/events.json","paper":"https://pith.science/paper/ZSAGVN2I"},"agent_actions":{"view_html":"https://pith.science/pith/ZSAGVN2IJKT2UJYB35TX3Z5DHF","download_json":"https://pith.science/pith/ZSAGVN2IJKT2UJYB35TX3Z5DHF.json","view_paper":"https://pith.science/paper/ZSAGVN2I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.14400&json=true","fetch_graph":"https://pith.science/api/pith-number/ZSAGVN2IJKT2UJYB35TX3Z5DHF/graph.json","fetch_events":"https://pith.science/api/pith-number/ZSAGVN2IJKT2UJYB35TX3Z5DHF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZSAGVN2IJKT2UJYB35TX3Z5DHF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZSAGVN2IJKT2UJYB35TX3Z5DHF/action/storage_attestation","attest_author":"https://pith.science/pith/ZSAGVN2IJKT2UJYB35TX3Z5DHF/action/author_attestation","sign_citation":"https://pith.science/pith/ZSAGVN2IJKT2UJYB35TX3Z5DHF/action/citation_signature","submit_replication":"https://pith.science/pith/ZSAGVN2IJKT2UJYB35TX3Z5DHF/action/replication_record"}},"created_at":"2026-07-05T06:18:21.895873+00:00","updated_at":"2026-07-05T06:18:21.895873+00:00"}