{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ZOIPVYO4KM4B5O6BGJNIKXNGDM","short_pith_number":"pith:ZOIPVYO4","schema_version":"1.0","canonical_sha256":"cb90fae1dc53381ebbc1325a855da61b0859cee8a8374de6d0354c3f05f64bf3","source":{"kind":"arxiv","id":"2303.09170","version":2},"attestation_state":"computed","paper":{"title":"NLUT: Neural-based 3D Lookup Tables for Video Photorealistic Style Transfer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Chaoping Xie, Han Yang, Wei Wang, Xuming Wen, Yaosen Chen, Yuegen Liu, Yuexin Yang","submitted_at":"2023-03-16T09:27:40Z","abstract_excerpt":"Video photorealistic style transfer is desired to generate videos with a similar photorealistic style to the style image while maintaining temporal consistency. However, existing methods obtain stylized video sequences by performing frame-by-frame photorealistic style transfer, which is inefficient and does not ensure the temporal consistency of the stylized video. To address this issue, we use neural network-based 3D Lookup Tables (LUTs) for the photorealistic transfer of videos, achieving a balance between efficiency and effectiveness. We first train a neural network for generating photoreal"},"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":"2303.09170","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-16T09:27:40Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"e27a59041dddd7a09fb17da9f2d9d2c8528f2169a965b2a2462ee151f0b1ab49","abstract_canon_sha256":"b72c02d4b7fc66ed15047b6bbc2c87fe562835b7ee61d0d9e65f2a8c912af276"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:51:58.259982Z","signature_b64":"OBePyMwNRSwoSuObkcOGmYJ9SdCJo6ohYoKIYLF2xlQFo/NUJjYiKDBWtOTBRIupyxvTsYeBt+OiqAyzUaKuCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cb90fae1dc53381ebbc1325a855da61b0859cee8a8374de6d0354c3f05f64bf3","last_reissued_at":"2026-07-05T05:51:58.259520Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:51:58.259520Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"NLUT: Neural-based 3D Lookup Tables for Video Photorealistic Style Transfer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Chaoping Xie, Han Yang, Wei Wang, Xuming Wen, Yaosen Chen, Yuegen Liu, Yuexin Yang","submitted_at":"2023-03-16T09:27:40Z","abstract_excerpt":"Video photorealistic style transfer is desired to generate videos with a similar photorealistic style to the style image while maintaining temporal consistency. However, existing methods obtain stylized video sequences by performing frame-by-frame photorealistic style transfer, which is inefficient and does not ensure the temporal consistency of the stylized video. To address this issue, we use neural network-based 3D Lookup Tables (LUTs) for the photorealistic transfer of videos, achieving a balance between efficiency and effectiveness. We first train a neural network for generating photoreal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.09170","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/2303.09170/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":"2303.09170","created_at":"2026-07-05T05:51:58.259579+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.09170v2","created_at":"2026-07-05T05:51:58.259579+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.09170","created_at":"2026-07-05T05:51:58.259579+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZOIPVYO4KM4B","created_at":"2026-07-05T05:51:58.259579+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZOIPVYO4KM4B5O6B","created_at":"2026-07-05T05:51:58.259579+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZOIPVYO4","created_at":"2026-07-05T05:51:58.259579+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.01638","citing_title":"CanonCGT: Reference-Based Color Grading via Canonical Pivot Representation","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZOIPVYO4KM4B5O6BGJNIKXNGDM","json":"https://pith.science/pith/ZOIPVYO4KM4B5O6BGJNIKXNGDM.json","graph_json":"https://pith.science/api/pith-number/ZOIPVYO4KM4B5O6BGJNIKXNGDM/graph.json","events_json":"https://pith.science/api/pith-number/ZOIPVYO4KM4B5O6BGJNIKXNGDM/events.json","paper":"https://pith.science/paper/ZOIPVYO4"},"agent_actions":{"view_html":"https://pith.science/pith/ZOIPVYO4KM4B5O6BGJNIKXNGDM","download_json":"https://pith.science/pith/ZOIPVYO4KM4B5O6BGJNIKXNGDM.json","view_paper":"https://pith.science/paper/ZOIPVYO4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.09170&json=true","fetch_graph":"https://pith.science/api/pith-number/ZOIPVYO4KM4B5O6BGJNIKXNGDM/graph.json","fetch_events":"https://pith.science/api/pith-number/ZOIPVYO4KM4B5O6BGJNIKXNGDM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZOIPVYO4KM4B5O6BGJNIKXNGDM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZOIPVYO4KM4B5O6BGJNIKXNGDM/action/storage_attestation","attest_author":"https://pith.science/pith/ZOIPVYO4KM4B5O6BGJNIKXNGDM/action/author_attestation","sign_citation":"https://pith.science/pith/ZOIPVYO4KM4B5O6BGJNIKXNGDM/action/citation_signature","submit_replication":"https://pith.science/pith/ZOIPVYO4KM4B5O6BGJNIKXNGDM/action/replication_record"}},"created_at":"2026-07-05T05:51:58.259579+00:00","updated_at":"2026-07-05T05:51:58.259579+00:00"}