{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:C6XNDRR7Q7WA6YVT3W3C6YNDBH","short_pith_number":"pith:C6XNDRR7","schema_version":"1.0","canonical_sha256":"17aed1c63f87ec0f62b3ddb62f61a309f2781d46072ea9ef144de624485f42ba","source":{"kind":"arxiv","id":"2502.08642","version":1},"attestation_state":"computed","paper":{"title":"SwiftSketch: A Diffusion Model for Image-to-Vector Sketch Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ariel Shamir, Daniel Cohen-Or, Ellie Arar, Yael Vinker, Yarden Frenkel","submitted_at":"2025-02-12T18:57:12Z","abstract_excerpt":"Recent advancements in large vision-language models have enabled highly expressive and diverse vector sketch generation. However, state-of-the-art methods rely on a time-consuming optimization process involving repeated feedback from a pretrained model to determine stroke placement. Consequently, despite producing impressive sketches, these methods are limited in practical applications. In this work, we introduce SwiftSketch, a diffusion model for image-conditioned vector sketch generation that can produce high-quality sketches in less than a second. SwiftSketch operates by progressively denoi"},"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":"2502.08642","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-02-12T18:57:12Z","cross_cats_sorted":[],"title_canon_sha256":"68c09aa9ac14dd4f83de4d65624affd001415e06f88356cdde2059c85d39610a","abstract_canon_sha256":"6980a37caf0e8631e4df6e620f6551858c6392dbb503eba2a6b7577ed8900217"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:13:25.767648Z","signature_b64":"v0VAlz0cD43QKa2o8oPrkA028L/CVTzwvMo1DFaCVm+rX+FjJvo+Fe/cJJY68VUWFV/bPEMghLxp7YnJ4daZDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"17aed1c63f87ec0f62b3ddb62f61a309f2781d46072ea9ef144de624485f42ba","last_reissued_at":"2026-07-05T10:13:25.767153Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:13:25.767153Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SwiftSketch: A Diffusion Model for Image-to-Vector Sketch Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ariel Shamir, Daniel Cohen-Or, Ellie Arar, Yael Vinker, Yarden Frenkel","submitted_at":"2025-02-12T18:57:12Z","abstract_excerpt":"Recent advancements in large vision-language models have enabled highly expressive and diverse vector sketch generation. However, state-of-the-art methods rely on a time-consuming optimization process involving repeated feedback from a pretrained model to determine stroke placement. Consequently, despite producing impressive sketches, these methods are limited in practical applications. In this work, we introduce SwiftSketch, a diffusion model for image-conditioned vector sketch generation that can produce high-quality sketches in less than a second. SwiftSketch operates by progressively denoi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.08642","kind":"arxiv","version":1},"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/2502.08642/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":"2502.08642","created_at":"2026-07-05T10:13:25.767210+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.08642v1","created_at":"2026-07-05T10:13:25.767210+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.08642","created_at":"2026-07-05T10:13:25.767210+00:00"},{"alias_kind":"pith_short_12","alias_value":"C6XNDRR7Q7WA","created_at":"2026-07-05T10:13:25.767210+00:00"},{"alias_kind":"pith_short_16","alias_value":"C6XNDRR7Q7WA6YVT","created_at":"2026-07-05T10:13:25.767210+00:00"},{"alias_kind":"pith_short_8","alias_value":"C6XNDRR7","created_at":"2026-07-05T10:13:25.767210+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.19084","citing_title":"Jodi: Unification of Visual Generation and Understanding via Joint Modeling","ref_index":3,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/C6XNDRR7Q7WA6YVT3W3C6YNDBH","json":"https://pith.science/pith/C6XNDRR7Q7WA6YVT3W3C6YNDBH.json","graph_json":"https://pith.science/api/pith-number/C6XNDRR7Q7WA6YVT3W3C6YNDBH/graph.json","events_json":"https://pith.science/api/pith-number/C6XNDRR7Q7WA6YVT3W3C6YNDBH/events.json","paper":"https://pith.science/paper/C6XNDRR7"},"agent_actions":{"view_html":"https://pith.science/pith/C6XNDRR7Q7WA6YVT3W3C6YNDBH","download_json":"https://pith.science/pith/C6XNDRR7Q7WA6YVT3W3C6YNDBH.json","view_paper":"https://pith.science/paper/C6XNDRR7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.08642&json=true","fetch_graph":"https://pith.science/api/pith-number/C6XNDRR7Q7WA6YVT3W3C6YNDBH/graph.json","fetch_events":"https://pith.science/api/pith-number/C6XNDRR7Q7WA6YVT3W3C6YNDBH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C6XNDRR7Q7WA6YVT3W3C6YNDBH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C6XNDRR7Q7WA6YVT3W3C6YNDBH/action/storage_attestation","attest_author":"https://pith.science/pith/C6XNDRR7Q7WA6YVT3W3C6YNDBH/action/author_attestation","sign_citation":"https://pith.science/pith/C6XNDRR7Q7WA6YVT3W3C6YNDBH/action/citation_signature","submit_replication":"https://pith.science/pith/C6XNDRR7Q7WA6YVT3W3C6YNDBH/action/replication_record"}},"created_at":"2026-07-05T10:13:25.767210+00:00","updated_at":"2026-07-05T10:13:25.767210+00:00"}