{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:JAN67BLYG4QURVHGYFUET3AXL5","short_pith_number":"pith:JAN67BLY","schema_version":"1.0","canonical_sha256":"481bef8578372148d4e6c16849ec175f7fd0940fee3833eb9c4efa0c3bdc0c8d","source":{"kind":"arxiv","id":"2305.15732","version":2},"attestation_state":"computed","paper":{"title":"CLIP3Dstyler: Language Guided 3D Arbitrary Neural Style Transfer","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chenkai Zhao, Ming Gao, Mingming Gong, Tingbo Hou, Yang Zhao, Yanwu Xu","submitted_at":"2023-05-25T05:30:13Z","abstract_excerpt":"In this paper, we propose a novel language-guided 3D arbitrary neural style transfer method (CLIP3Dstyler). We aim at stylizing any 3D scene with an arbitrary style from a text description, and synthesizing the novel stylized view, which is more flexible than the image-conditioned style transfer. Compared with the previous 2D method CLIPStyler, we are able to stylize a 3D scene and generalize to novel scenes without re-train our model. A straightforward solution is to combine previous image-conditioned 3D style transfer and text-conditioned 2D style transfer \\bigskip methods. However, such a s"},"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":"2305.15732","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-05-25T05:30:13Z","cross_cats_sorted":[],"title_canon_sha256":"43fbdf4b799c308f89c546bd56637434e3552ba018f6d4ab2977f7151ff72a6e","abstract_canon_sha256":"faba0da4334a35af7b3ed1819ecf21b5c095f30f8ea8b4c935953d4fce2a3cc7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:14:11.448216Z","signature_b64":"d8N+iBd25UAAEwD+bbR6Mlg2/TDphiJoZ2C6DUzG2EJdlt8AnoLYs7tbPzhNdf5TWTNv/DlwpgaJ+89RDMOdDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"481bef8578372148d4e6c16849ec175f7fd0940fee3833eb9c4efa0c3bdc0c8d","last_reissued_at":"2026-07-05T06:14:11.447814Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:14:11.447814Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CLIP3Dstyler: Language Guided 3D Arbitrary Neural Style Transfer","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chenkai Zhao, Ming Gao, Mingming Gong, Tingbo Hou, Yang Zhao, Yanwu Xu","submitted_at":"2023-05-25T05:30:13Z","abstract_excerpt":"In this paper, we propose a novel language-guided 3D arbitrary neural style transfer method (CLIP3Dstyler). We aim at stylizing any 3D scene with an arbitrary style from a text description, and synthesizing the novel stylized view, which is more flexible than the image-conditioned style transfer. Compared with the previous 2D method CLIPStyler, we are able to stylize a 3D scene and generalize to novel scenes without re-train our model. A straightforward solution is to combine previous image-conditioned 3D style transfer and text-conditioned 2D style transfer \\bigskip methods. However, such a s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.15732","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/2305.15732/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":"2305.15732","created_at":"2026-07-05T06:14:11.447867+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.15732v2","created_at":"2026-07-05T06:14:11.447867+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.15732","created_at":"2026-07-05T06:14:11.447867+00:00"},{"alias_kind":"pith_short_12","alias_value":"JAN67BLYG4QU","created_at":"2026-07-05T06:14:11.447867+00:00"},{"alias_kind":"pith_short_16","alias_value":"JAN67BLYG4QURVHG","created_at":"2026-07-05T06:14:11.447867+00:00"},{"alias_kind":"pith_short_8","alias_value":"JAN67BLY","created_at":"2026-07-05T06:14:11.447867+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JAN67BLYG4QURVHGYFUET3AXL5","json":"https://pith.science/pith/JAN67BLYG4QURVHGYFUET3AXL5.json","graph_json":"https://pith.science/api/pith-number/JAN67BLYG4QURVHGYFUET3AXL5/graph.json","events_json":"https://pith.science/api/pith-number/JAN67BLYG4QURVHGYFUET3AXL5/events.json","paper":"https://pith.science/paper/JAN67BLY"},"agent_actions":{"view_html":"https://pith.science/pith/JAN67BLYG4QURVHGYFUET3AXL5","download_json":"https://pith.science/pith/JAN67BLYG4QURVHGYFUET3AXL5.json","view_paper":"https://pith.science/paper/JAN67BLY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.15732&json=true","fetch_graph":"https://pith.science/api/pith-number/JAN67BLYG4QURVHGYFUET3AXL5/graph.json","fetch_events":"https://pith.science/api/pith-number/JAN67BLYG4QURVHGYFUET3AXL5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JAN67BLYG4QURVHGYFUET3AXL5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JAN67BLYG4QURVHGYFUET3AXL5/action/storage_attestation","attest_author":"https://pith.science/pith/JAN67BLYG4QURVHGYFUET3AXL5/action/author_attestation","sign_citation":"https://pith.science/pith/JAN67BLYG4QURVHGYFUET3AXL5/action/citation_signature","submit_replication":"https://pith.science/pith/JAN67BLYG4QURVHGYFUET3AXL5/action/replication_record"}},"created_at":"2026-07-05T06:14:11.447867+00:00","updated_at":"2026-07-05T06:14:11.447867+00:00"}