{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:XHU4NNUYJCLC7ZHDZO2WDQNTZ2","short_pith_number":"pith:XHU4NNUY","schema_version":"1.0","canonical_sha256":"b9e9c6b69848962fe4e3cbb561c1b3ce8757c7650e5dc46892715938bbf80f08","source":{"kind":"arxiv","id":"2503.08434","version":5},"attestation_state":"computed","paper":{"title":"Bokeh Diffusion: Defocus Blur Control in Text-to-Image Diffusion Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.GR","authors_text":"Armando Fortes, Shangchen Zhou, Tianyi Wei, Xingang Pan","submitted_at":"2025-03-11T13:49:12Z","abstract_excerpt":"Recent advances in large-scale text-to-image models have revolutionized creative fields by generating visually captivating outputs from textual prompts; however, while traditional photography offers precise control over camera settings to shape visual aesthetics - such as depth-of-field via aperture - current diffusion models typically rely on prompt engineering to mimic such effects. This approach often results in crude approximations and inadvertently alters the scene content. In this work, we propose Bokeh Diffusion, a scene-consistent bokeh control framework that explicitly conditions a di"},"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":"2503.08434","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2025-03-11T13:49:12Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"dec645517123bb733289febdf1a8ddd9f66c0fd2b03cd1e4857aabed93bc7435","abstract_canon_sha256":"21a7348fca7a6076fc6a320bd276b60f36309ae05dd1333ebf1063994e8cb5ed"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T01:04:38.048424Z","signature_b64":"I1Ts0UUtXzdFsgRh6V1Pm2PZ40eR6l1QG0zhG25FgLpb/FY6C3QPlLEzjB0FJzDAZKuE9EYxc8Lk/7qywZjOCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b9e9c6b69848962fe4e3cbb561c1b3ce8757c7650e5dc46892715938bbf80f08","last_reissued_at":"2026-06-09T01:04:38.047945Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T01:04:38.047945Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bokeh Diffusion: Defocus Blur Control in Text-to-Image Diffusion Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.GR","authors_text":"Armando Fortes, Shangchen Zhou, Tianyi Wei, Xingang Pan","submitted_at":"2025-03-11T13:49:12Z","abstract_excerpt":"Recent advances in large-scale text-to-image models have revolutionized creative fields by generating visually captivating outputs from textual prompts; however, while traditional photography offers precise control over camera settings to shape visual aesthetics - such as depth-of-field via aperture - current diffusion models typically rely on prompt engineering to mimic such effects. This approach often results in crude approximations and inadvertently alters the scene content. In this work, we propose Bokeh Diffusion, a scene-consistent bokeh control framework that explicitly conditions a di"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.08434","kind":"arxiv","version":5},"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/2503.08434/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":"2503.08434","created_at":"2026-06-09T01:04:38.047998+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.08434v5","created_at":"2026-06-09T01:04:38.047998+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.08434","created_at":"2026-06-09T01:04:38.047998+00:00"},{"alias_kind":"pith_short_12","alias_value":"XHU4NNUYJCLC","created_at":"2026-06-09T01:04:38.047998+00:00"},{"alias_kind":"pith_short_16","alias_value":"XHU4NNUYJCLC7ZHD","created_at":"2026-06-09T01:04:38.047998+00:00"},{"alias_kind":"pith_short_8","alias_value":"XHU4NNUY","created_at":"2026-06-09T01:04:38.047998+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2511.17844","citing_title":"Less is More: Data-Efficient Adaptation for Controllable Text-to-Video Generation","ref_index":7,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XHU4NNUYJCLC7ZHDZO2WDQNTZ2","json":"https://pith.science/pith/XHU4NNUYJCLC7ZHDZO2WDQNTZ2.json","graph_json":"https://pith.science/api/pith-number/XHU4NNUYJCLC7ZHDZO2WDQNTZ2/graph.json","events_json":"https://pith.science/api/pith-number/XHU4NNUYJCLC7ZHDZO2WDQNTZ2/events.json","paper":"https://pith.science/paper/XHU4NNUY"},"agent_actions":{"view_html":"https://pith.science/pith/XHU4NNUYJCLC7ZHDZO2WDQNTZ2","download_json":"https://pith.science/pith/XHU4NNUYJCLC7ZHDZO2WDQNTZ2.json","view_paper":"https://pith.science/paper/XHU4NNUY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.08434&json=true","fetch_graph":"https://pith.science/api/pith-number/XHU4NNUYJCLC7ZHDZO2WDQNTZ2/graph.json","fetch_events":"https://pith.science/api/pith-number/XHU4NNUYJCLC7ZHDZO2WDQNTZ2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XHU4NNUYJCLC7ZHDZO2WDQNTZ2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XHU4NNUYJCLC7ZHDZO2WDQNTZ2/action/storage_attestation","attest_author":"https://pith.science/pith/XHU4NNUYJCLC7ZHDZO2WDQNTZ2/action/author_attestation","sign_citation":"https://pith.science/pith/XHU4NNUYJCLC7ZHDZO2WDQNTZ2/action/citation_signature","submit_replication":"https://pith.science/pith/XHU4NNUYJCLC7ZHDZO2WDQNTZ2/action/replication_record"}},"created_at":"2026-06-09T01:04:38.047998+00:00","updated_at":"2026-06-09T01:04:38.047998+00:00"}