{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:PFUWKUECSVHUEWPYIJMC4ONO6Y","short_pith_number":"pith:PFUWKUEC","canonical_record":{"source":{"id":"2310.02596","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-04T05:59:50Z","cross_cats_sorted":[],"title_canon_sha256":"4aaf8b46e4001cb7408cb379099be1aa53126acc201ca0f2360d489b0173e91c","abstract_canon_sha256":"116e40d3c1b7f8c949fe070663fc419c54e63b169224359c4dedd7a4e260be91"},"schema_version":"1.0"},"canonical_sha256":"7969655082954f4259f842582e39aef61a16ebe40fa1c1021e89c71c6d4a0759","source":{"kind":"arxiv","id":"2310.02596","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.02596","created_at":"2026-07-05T07:02:54Z"},{"alias_kind":"arxiv_version","alias_value":"2310.02596v2","created_at":"2026-07-05T07:02:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.02596","created_at":"2026-07-05T07:02:54Z"},{"alias_kind":"pith_short_12","alias_value":"PFUWKUECSVHU","created_at":"2026-07-05T07:02:54Z"},{"alias_kind":"pith_short_16","alias_value":"PFUWKUECSVHUEWPY","created_at":"2026-07-05T07:02:54Z"},{"alias_kind":"pith_short_8","alias_value":"PFUWKUEC","created_at":"2026-07-05T07:02:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:PFUWKUECSVHUEWPYIJMC4ONO6Y","target":"record","payload":{"canonical_record":{"source":{"id":"2310.02596","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-04T05:59:50Z","cross_cats_sorted":[],"title_canon_sha256":"4aaf8b46e4001cb7408cb379099be1aa53126acc201ca0f2360d489b0173e91c","abstract_canon_sha256":"116e40d3c1b7f8c949fe070663fc419c54e63b169224359c4dedd7a4e260be91"},"schema_version":"1.0"},"canonical_sha256":"7969655082954f4259f842582e39aef61a16ebe40fa1c1021e89c71c6d4a0759","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:02:54.135288Z","signature_b64":"+4v8ExlwMB2bGcaRjw+I8Xmx0qm3yAMyQVf7U1gyI62KkE0mORmtRUu4SGYMzbh4sRBt/56dEjp+IcCnjIFaDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7969655082954f4259f842582e39aef61a16ebe40fa1c1021e89c71c6d4a0759","last_reissued_at":"2026-07-05T07:02:54.134777Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:02:54.134777Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.02596","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:02:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tVnnvQB5o0dfek9sc1MCsSvUEbD6jiILxHYEYG6b2Vspz4Z0oaHDSQHu9LFRB6HRaz2GzQzCJhvP5Z7EOaWBAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T07:39:35.309220Z"},"content_sha256":"1594e09560c6a8f6c55deff27bc868bc12e5aab39a4928be232138649b004644","schema_version":"1.0","event_id":"sha256:1594e09560c6a8f6c55deff27bc868bc12e5aab39a4928be232138649b004644"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:PFUWKUECSVHUEWPYIJMC4ONO6Y","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SweetDreamer: Aligning Geometric Priors in 2D Diffusion for Consistent Text-to-3D","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ping Tan, Rui Chen, Weiyu Li, Xuelin Chen","submitted_at":"2023-10-04T05:59:50Z","abstract_excerpt":"It is inherently ambiguous to lift 2D results from pre-trained diffusion models to a 3D world for text-to-3D generation. 2D diffusion models solely learn view-agnostic priors and thus lack 3D knowledge during the lifting, leading to the multi-view inconsistency problem. We find that this problem primarily stems from geometric inconsistency, and avoiding misplaced geometric structures substantially mitigates the problem in the final outputs. Therefore, we improve the consistency by aligning the 2D geometric priors in diffusion models with well-defined 3D shapes during the lifting, addressing th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.02596","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/2310.02596/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:02:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EFdyMOyXDROdQsXq2U8FeiPB2adhmkZRDZHQgm57VmP+NqQkyCgBGMPHa5QwPOlHm/ZLUYpHr6fjbtE+RNbbAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T07:39:35.310315Z"},"content_sha256":"f8b78e0b1295ac73b44d2d9eb93b797a3a7e63fcc0560c5b50fcfab00de0410f","schema_version":"1.0","event_id":"sha256:f8b78e0b1295ac73b44d2d9eb93b797a3a7e63fcc0560c5b50fcfab00de0410f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PFUWKUECSVHUEWPYIJMC4ONO6Y/bundle.json","state_url":"https://pith.science/pith/PFUWKUECSVHUEWPYIJMC4ONO6Y/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PFUWKUECSVHUEWPYIJMC4ONO6Y/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-07T07:39:35Z","links":{"resolver":"https://pith.science/pith/PFUWKUECSVHUEWPYIJMC4ONO6Y","bundle":"https://pith.science/pith/PFUWKUECSVHUEWPYIJMC4ONO6Y/bundle.json","state":"https://pith.science/pith/PFUWKUECSVHUEWPYIJMC4ONO6Y/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PFUWKUECSVHUEWPYIJMC4ONO6Y/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:PFUWKUECSVHUEWPYIJMC4ONO6Y","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"116e40d3c1b7f8c949fe070663fc419c54e63b169224359c4dedd7a4e260be91","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-04T05:59:50Z","title_canon_sha256":"4aaf8b46e4001cb7408cb379099be1aa53126acc201ca0f2360d489b0173e91c"},"schema_version":"1.0","source":{"id":"2310.02596","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.02596","created_at":"2026-07-05T07:02:54Z"},{"alias_kind":"arxiv_version","alias_value":"2310.02596v2","created_at":"2026-07-05T07:02:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.02596","created_at":"2026-07-05T07:02:54Z"},{"alias_kind":"pith_short_12","alias_value":"PFUWKUECSVHU","created_at":"2026-07-05T07:02:54Z"},{"alias_kind":"pith_short_16","alias_value":"PFUWKUECSVHUEWPY","created_at":"2026-07-05T07:02:54Z"},{"alias_kind":"pith_short_8","alias_value":"PFUWKUEC","created_at":"2026-07-05T07:02:54Z"}],"graph_snapshots":[{"event_id":"sha256:f8b78e0b1295ac73b44d2d9eb93b797a3a7e63fcc0560c5b50fcfab00de0410f","target":"graph","created_at":"2026-07-05T07:02:54Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2310.02596/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"It is inherently ambiguous to lift 2D results from pre-trained diffusion models to a 3D world for text-to-3D generation. 2D diffusion models solely learn view-agnostic priors and thus lack 3D knowledge during the lifting, leading to the multi-view inconsistency problem. We find that this problem primarily stems from geometric inconsistency, and avoiding misplaced geometric structures substantially mitigates the problem in the final outputs. Therefore, we improve the consistency by aligning the 2D geometric priors in diffusion models with well-defined 3D shapes during the lifting, addressing th","authors_text":"Ping Tan, Rui Chen, Weiyu Li, Xuelin Chen","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-04T05:59:50Z","title":"SweetDreamer: Aligning Geometric Priors in 2D Diffusion for Consistent Text-to-3D"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.02596","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1594e09560c6a8f6c55deff27bc868bc12e5aab39a4928be232138649b004644","target":"record","created_at":"2026-07-05T07:02:54Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"116e40d3c1b7f8c949fe070663fc419c54e63b169224359c4dedd7a4e260be91","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-04T05:59:50Z","title_canon_sha256":"4aaf8b46e4001cb7408cb379099be1aa53126acc201ca0f2360d489b0173e91c"},"schema_version":"1.0","source":{"id":"2310.02596","kind":"arxiv","version":2}},"canonical_sha256":"7969655082954f4259f842582e39aef61a16ebe40fa1c1021e89c71c6d4a0759","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7969655082954f4259f842582e39aef61a16ebe40fa1c1021e89c71c6d4a0759","first_computed_at":"2026-07-05T07:02:54.134777Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:02:54.134777Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+4v8ExlwMB2bGcaRjw+I8Xmx0qm3yAMyQVf7U1gyI62KkE0mORmtRUu4SGYMzbh4sRBt/56dEjp+IcCnjIFaDg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:02:54.135288Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.02596","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1594e09560c6a8f6c55deff27bc868bc12e5aab39a4928be232138649b004644","sha256:f8b78e0b1295ac73b44d2d9eb93b797a3a7e63fcc0560c5b50fcfab00de0410f"],"state_sha256":"798b56ef3ed2df9e6a3c337b6e94b98c2d126763b061d4bd8f062a2dace929df"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yXft+ylPi9DPMKnx+k4HHSPgaV1H8ls/WaH2gpiRvsBEcVU9gwZxeuwItZ+ET4q7mkFZ04zuqs7cI+rqkBSHCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T07:39:35.326494Z","bundle_sha256":"b1e2166e3cbf553eecc88d7081bdafe2d289630b4ee608ede96cfd9dad4ddc48"}}