{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:3OVJACD2KUIRPK6X6XXRSRJ44W","short_pith_number":"pith:3OVJACD2","schema_version":"1.0","canonical_sha256":"dbaa90087a551117abd7f5ef19453ce58a71b80fed6988fb9b45559a98f96b8d","source":{"kind":"arxiv","id":"2310.10343","version":1},"attestation_state":"computed","paper":{"title":"ConsistNet: Enforcing 3D Consistency for Multi-view Images Diffusion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongdong Li, Jiayu Yang, Pan Ji, Yunfei Duan, Ziang Cheng","submitted_at":"2023-10-16T12:29:29Z","abstract_excerpt":"Given a single image of a 3D object, this paper proposes a novel method (named ConsistNet) that is able to generate multiple images of the same object, as if seen they are captured from different viewpoints, while the 3D (multi-view) consistencies among those multiple generated images are effectively exploited. Central to our method is a multi-view consistency block which enables information exchange across multiple single-view diffusion processes based on the underlying multi-view geometry principles. ConsistNet is an extension to the standard latent diffusion model, and consists of two sub-m"},"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":"2310.10343","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-16T12:29:29Z","cross_cats_sorted":[],"title_canon_sha256":"ec4b4958f8102df9cc465c133b0348efe464aad946e0e3f5b3292ef93cefff34","abstract_canon_sha256":"f288294ab5f041bf2f48c6851876a7739868ab2b877d54f535047d372020524c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:01:19.021335Z","signature_b64":"yjEPBNvJCebNfQ967ZjBEX1vOUy91e3fcHdmk53aSXAOTlZhL1rRleZRCSvIwV7111WCNq93E+t1SyhKsJMkCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dbaa90087a551117abd7f5ef19453ce58a71b80fed6988fb9b45559a98f96b8d","last_reissued_at":"2026-07-05T07:01:19.020936Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:01:19.020936Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ConsistNet: Enforcing 3D Consistency for Multi-view Images Diffusion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongdong Li, Jiayu Yang, Pan Ji, Yunfei Duan, Ziang Cheng","submitted_at":"2023-10-16T12:29:29Z","abstract_excerpt":"Given a single image of a 3D object, this paper proposes a novel method (named ConsistNet) that is able to generate multiple images of the same object, as if seen they are captured from different viewpoints, while the 3D (multi-view) consistencies among those multiple generated images are effectively exploited. Central to our method is a multi-view consistency block which enables information exchange across multiple single-view diffusion processes based on the underlying multi-view geometry principles. ConsistNet is an extension to the standard latent diffusion model, and consists of two sub-m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.10343","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/2310.10343/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":"2310.10343","created_at":"2026-07-05T07:01:19.020992+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.10343v1","created_at":"2026-07-05T07:01:19.020992+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.10343","created_at":"2026-07-05T07:01:19.020992+00:00"},{"alias_kind":"pith_short_12","alias_value":"3OVJACD2KUIR","created_at":"2026-07-05T07:01:19.020992+00:00"},{"alias_kind":"pith_short_16","alias_value":"3OVJACD2KUIRPK6X","created_at":"2026-07-05T07:01:19.020992+00:00"},{"alias_kind":"pith_short_8","alias_value":"3OVJACD2","created_at":"2026-07-05T07:01:19.020992+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2405.10314","citing_title":"CAT3D: Create Anything in 3D with Multi-View Diffusion Models","ref_index":45,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02583","citing_title":"Stylistic Attribute Control in Latent Diffusion Models","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3OVJACD2KUIRPK6X6XXRSRJ44W","json":"https://pith.science/pith/3OVJACD2KUIRPK6X6XXRSRJ44W.json","graph_json":"https://pith.science/api/pith-number/3OVJACD2KUIRPK6X6XXRSRJ44W/graph.json","events_json":"https://pith.science/api/pith-number/3OVJACD2KUIRPK6X6XXRSRJ44W/events.json","paper":"https://pith.science/paper/3OVJACD2"},"agent_actions":{"view_html":"https://pith.science/pith/3OVJACD2KUIRPK6X6XXRSRJ44W","download_json":"https://pith.science/pith/3OVJACD2KUIRPK6X6XXRSRJ44W.json","view_paper":"https://pith.science/paper/3OVJACD2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.10343&json=true","fetch_graph":"https://pith.science/api/pith-number/3OVJACD2KUIRPK6X6XXRSRJ44W/graph.json","fetch_events":"https://pith.science/api/pith-number/3OVJACD2KUIRPK6X6XXRSRJ44W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3OVJACD2KUIRPK6X6XXRSRJ44W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3OVJACD2KUIRPK6X6XXRSRJ44W/action/storage_attestation","attest_author":"https://pith.science/pith/3OVJACD2KUIRPK6X6XXRSRJ44W/action/author_attestation","sign_citation":"https://pith.science/pith/3OVJACD2KUIRPK6X6XXRSRJ44W/action/citation_signature","submit_replication":"https://pith.science/pith/3OVJACD2KUIRPK6X6XXRSRJ44W/action/replication_record"}},"created_at":"2026-07-05T07:01:19.020992+00:00","updated_at":"2026-07-05T07:01:19.020992+00:00"}