{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:7GRW2ZTVIYCYEU6SJBTAOIGKRJ","short_pith_number":"pith:7GRW2ZTV","schema_version":"1.0","canonical_sha256":"f9a36d667546058253d248660720ca8a5c9ea0ae2df0b599d65178b14158208d","source":{"kind":"arxiv","id":"2608.03179","version":1},"attestation_state":"computed","paper":{"title":"EditFlow3D: Automated Local Editing of 3D Assets with Trajectory Preservation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Buyu Li, Chuang Wang, Haitao Zhou, Jiahe Song, Qian Yu, Rui Nie, Sheng Wang","submitted_at":"2026-08-04T06:16:17Z","abstract_excerpt":"Controllable local editing of 3D assets requires precise target localization and appropriate visual guidance. However, existing methods lack a simple yet accurate way to obtain 3D masks and struggle to achieve the desired edit while faithfully preserving the structure and appearance of non-target regions. To address these challenges, we present EditFlow3D, a training-free framework for local 3D editing. Given a source asset and an edit instruction, a VLM-driven workflow interprets the editing intent and automatically constructs a visual guidance image and a refined 3D editing mask, enabling lo"},"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":"2608.03179","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-08-04T06:16:17Z","cross_cats_sorted":[],"title_canon_sha256":"08fb57cbdfbbfe324b02bf710a82fe3de68210a5ca2793a89e9c1f2935692528","abstract_canon_sha256":"086ea4a8c89293b2864c3ae25e40855bffe0f1399a6e7b2ee4f5d2c2ee162887"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-05T00:46:03.156079Z","signature_b64":"5dtRgf7UBZuaMhtshJ6F+rXzoI3G/0r25ZhWbov6rDTp7VXwwBigZ/HKkSVCiltFkRdTBFjWjbZCyDdb846XDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f9a36d667546058253d248660720ca8a5c9ea0ae2df0b599d65178b14158208d","last_reissued_at":"2026-08-05T00:46:03.153762Z","signature_status":"signed_v1","first_computed_at":"2026-08-05T00:46:03.153762Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EditFlow3D: Automated Local Editing of 3D Assets with Trajectory Preservation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Buyu Li, Chuang Wang, Haitao Zhou, Jiahe Song, Qian Yu, Rui Nie, Sheng Wang","submitted_at":"2026-08-04T06:16:17Z","abstract_excerpt":"Controllable local editing of 3D assets requires precise target localization and appropriate visual guidance. However, existing methods lack a simple yet accurate way to obtain 3D masks and struggle to achieve the desired edit while faithfully preserving the structure and appearance of non-target regions. To address these challenges, we present EditFlow3D, a training-free framework for local 3D editing. Given a source asset and an edit instruction, a VLM-driven workflow interprets the editing intent and automatically constructs a visual guidance image and a refined 3D editing mask, enabling lo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.03179","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/2608.03179/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":"2608.03179","created_at":"2026-08-05T00:46:03.154575+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.03179v1","created_at":"2026-08-05T00:46:03.154575+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.03179","created_at":"2026-08-05T00:46:03.154575+00:00"},{"alias_kind":"pith_short_12","alias_value":"7GRW2ZTVIYCY","created_at":"2026-08-05T00:46:03.154575+00:00"},{"alias_kind":"pith_short_16","alias_value":"7GRW2ZTVIYCYEU6S","created_at":"2026-08-05T00:46:03.154575+00:00"},{"alias_kind":"pith_short_8","alias_value":"7GRW2ZTV","created_at":"2026-08-05T00:46:03.154575+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/7GRW2ZTVIYCYEU6SJBTAOIGKRJ","json":"https://pith.science/pith/7GRW2ZTVIYCYEU6SJBTAOIGKRJ.json","graph_json":"https://pith.science/api/pith-number/7GRW2ZTVIYCYEU6SJBTAOIGKRJ/graph.json","events_json":"https://pith.science/api/pith-number/7GRW2ZTVIYCYEU6SJBTAOIGKRJ/events.json","paper":"https://pith.science/paper/7GRW2ZTV"},"agent_actions":{"view_html":"https://pith.science/pith/7GRW2ZTVIYCYEU6SJBTAOIGKRJ","download_json":"https://pith.science/pith/7GRW2ZTVIYCYEU6SJBTAOIGKRJ.json","view_paper":"https://pith.science/paper/7GRW2ZTV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.03179&json=true","fetch_graph":"https://pith.science/api/pith-number/7GRW2ZTVIYCYEU6SJBTAOIGKRJ/graph.json","fetch_events":"https://pith.science/api/pith-number/7GRW2ZTVIYCYEU6SJBTAOIGKRJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7GRW2ZTVIYCYEU6SJBTAOIGKRJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7GRW2ZTVIYCYEU6SJBTAOIGKRJ/action/storage_attestation","attest_author":"https://pith.science/pith/7GRW2ZTVIYCYEU6SJBTAOIGKRJ/action/author_attestation","sign_citation":"https://pith.science/pith/7GRW2ZTVIYCYEU6SJBTAOIGKRJ/action/citation_signature","submit_replication":"https://pith.science/pith/7GRW2ZTVIYCYEU6SJBTAOIGKRJ/action/replication_record"}},"created_at":"2026-08-05T00:46:03.154575+00:00","updated_at":"2026-08-05T00:46:03.154575+00:00"}