{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:VM25G26ROQJ7JRMESDODBGUOQ4","short_pith_number":"pith:VM25G26R","schema_version":"1.0","canonical_sha256":"ab35d36bd17413f4c58490dc309a8e8719c2740e19725c91c87c207fa73d5024","source":{"kind":"arxiv","id":"2311.14521","version":4},"attestation_state":"computed","paper":{"title":"GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chi Zhang, Feng Wang, Guosheng Lin, Huaping Liu, Lei Yang, Xiaofeng Yang, Yikai Wang, Yiwen Chen, Zhongang Cai, Zilong Chen","submitted_at":"2023-11-24T14:46:59Z","abstract_excerpt":"3D editing plays a crucial role in many areas such as gaming and virtual reality. Traditional 3D editing methods, which rely on representations like meshes and point clouds, often fall short in realistically depicting complex scenes. On the other hand, methods based on implicit 3D representations, like Neural Radiance Field (NeRF), render complex scenes effectively but suffer from slow processing speeds and limited control over specific scene areas. In response to these challenges, our paper presents GaussianEditor, an innovative and efficient 3D editing algorithm based on Gaussian Splatting ("},"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":"2311.14521","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-24T14:46:59Z","cross_cats_sorted":[],"title_canon_sha256":"264f7b52ccd071d8d0f1a4580ec9ace93c0630afbb3161b982c5538999ece524","abstract_canon_sha256":"250a49a70c50e0fa5fc235c5eca9715f8a1d8e5c1b3da9ef0efedc90aa780ac2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:26:18.876698Z","signature_b64":"bx1/ADFaqjIwjpUhf+hVy8crr9wJQsIwR6xzpO/UnLWdTjxvfmEmfMBEpKH79zrn2/mvnvoKNXB6N1kjVpoaAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ab35d36bd17413f4c58490dc309a8e8719c2740e19725c91c87c207fa73d5024","last_reissued_at":"2026-07-05T07:26:18.874035Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:26:18.874035Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chi Zhang, Feng Wang, Guosheng Lin, Huaping Liu, Lei Yang, Xiaofeng Yang, Yikai Wang, Yiwen Chen, Zhongang Cai, Zilong Chen","submitted_at":"2023-11-24T14:46:59Z","abstract_excerpt":"3D editing plays a crucial role in many areas such as gaming and virtual reality. Traditional 3D editing methods, which rely on representations like meshes and point clouds, often fall short in realistically depicting complex scenes. On the other hand, methods based on implicit 3D representations, like Neural Radiance Field (NeRF), render complex scenes effectively but suffer from slow processing speeds and limited control over specific scene areas. In response to these challenges, our paper presents GaussianEditor, an innovative and efficient 3D editing algorithm based on Gaussian Splatting ("},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.14521","kind":"arxiv","version":4},"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/2311.14521/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":"2311.14521","created_at":"2026-07-05T07:26:18.874105+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.14521v4","created_at":"2026-07-05T07:26:18.874105+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.14521","created_at":"2026-07-05T07:26:18.874105+00:00"},{"alias_kind":"pith_short_12","alias_value":"VM25G26ROQJ7","created_at":"2026-07-05T07:26:18.874105+00:00"},{"alias_kind":"pith_short_16","alias_value":"VM25G26ROQJ7JRME","created_at":"2026-07-05T07:26:18.874105+00:00"},{"alias_kind":"pith_short_8","alias_value":"VM25G26R","created_at":"2026-07-05T07:26:18.874105+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.30065","citing_title":"Boosting Zero-Shot 3D Style Transfer with 2D Pre-trained Priors","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07230","citing_title":"PhyEdit: Towards Real-World Object Manipulation via Physically-Grounded Image Editing","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2604.15862","citing_title":"Splats in Splats++: Robust and Generalizable 3D Gaussian Splatting Steganography","ref_index":49,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VM25G26ROQJ7JRMESDODBGUOQ4","json":"https://pith.science/pith/VM25G26ROQJ7JRMESDODBGUOQ4.json","graph_json":"https://pith.science/api/pith-number/VM25G26ROQJ7JRMESDODBGUOQ4/graph.json","events_json":"https://pith.science/api/pith-number/VM25G26ROQJ7JRMESDODBGUOQ4/events.json","paper":"https://pith.science/paper/VM25G26R"},"agent_actions":{"view_html":"https://pith.science/pith/VM25G26ROQJ7JRMESDODBGUOQ4","download_json":"https://pith.science/pith/VM25G26ROQJ7JRMESDODBGUOQ4.json","view_paper":"https://pith.science/paper/VM25G26R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.14521&json=true","fetch_graph":"https://pith.science/api/pith-number/VM25G26ROQJ7JRMESDODBGUOQ4/graph.json","fetch_events":"https://pith.science/api/pith-number/VM25G26ROQJ7JRMESDODBGUOQ4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VM25G26ROQJ7JRMESDODBGUOQ4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VM25G26ROQJ7JRMESDODBGUOQ4/action/storage_attestation","attest_author":"https://pith.science/pith/VM25G26ROQJ7JRMESDODBGUOQ4/action/author_attestation","sign_citation":"https://pith.science/pith/VM25G26ROQJ7JRMESDODBGUOQ4/action/citation_signature","submit_replication":"https://pith.science/pith/VM25G26ROQJ7JRMESDODBGUOQ4/action/replication_record"}},"created_at":"2026-07-05T07:26:18.874105+00:00","updated_at":"2026-07-05T07:26:18.874105+00:00"}