{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:BXNOIXNDFEPKVNLXF3BDSRPHLL","short_pith_number":"pith:BXNOIXND","canonical_record":{"source":{"id":"2305.15753","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-25T06:05:52Z","cross_cats_sorted":[],"title_canon_sha256":"41a05fcfc294922b8cd52abd10052a2a456adbcaa1fe1311b0081db8b4d22a76","abstract_canon_sha256":"63fb165799311c56f455bb23e361b916366f3a496ac746ffd19c236eef27f389"},"schema_version":"1.0"},"canonical_sha256":"0ddae45da3291eaab5772ec23945e75addea3597d95a7e77d3ec3b923d7310e4","source":{"kind":"arxiv","id":"2305.15753","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.15753","created_at":"2026-07-05T06:13:44Z"},{"alias_kind":"arxiv_version","alias_value":"2305.15753v1","created_at":"2026-07-05T06:13:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.15753","created_at":"2026-07-05T06:13:44Z"},{"alias_kind":"pith_short_12","alias_value":"BXNOIXNDFEPK","created_at":"2026-07-05T06:13:44Z"},{"alias_kind":"pith_short_16","alias_value":"BXNOIXNDFEPKVNLX","created_at":"2026-07-05T06:13:44Z"},{"alias_kind":"pith_short_8","alias_value":"BXNOIXND","created_at":"2026-07-05T06:13:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:BXNOIXNDFEPKVNLXF3BDSRPHLL","target":"record","payload":{"canonical_record":{"source":{"id":"2305.15753","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-25T06:05:52Z","cross_cats_sorted":[],"title_canon_sha256":"41a05fcfc294922b8cd52abd10052a2a456adbcaa1fe1311b0081db8b4d22a76","abstract_canon_sha256":"63fb165799311c56f455bb23e361b916366f3a496ac746ffd19c236eef27f389"},"schema_version":"1.0"},"canonical_sha256":"0ddae45da3291eaab5772ec23945e75addea3597d95a7e77d3ec3b923d7310e4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:13:44.312691Z","signature_b64":"CmSPSMz4xxOywVy39DElJVwn2uIT3gWE5j5Fa8R/z81XzKiigqZdidjlhZZ7+1QFDDA6oLSW6oRg80PGRYEtAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ddae45da3291eaab5772ec23945e75addea3597d95a7e77d3ec3b923d7310e4","last_reissued_at":"2026-07-05T06:13:44.312240Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:13:44.312240Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.15753","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:13:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hLrYxbfuB/qwt1dlX9nPZvRCKtHevtpSLP3HjeHNWps8qqBTtrx2f5XKtDNaQTglHSAM+Ujw34TlfvIwFFE2Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T07:08:45.982549Z"},"content_sha256":"7e0c7354b8608cade77d8dfde358147351d7bcb93830e26bb06fc8518cfa5006","schema_version":"1.0","event_id":"sha256:7e0c7354b8608cade77d8dfde358147351d7bcb93830e26bb06fc8518cfa5006"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:BXNOIXNDFEPKVNLXF3BDSRPHLL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"T2TD: Text-3D Generation Model based on Prior Knowledge Guidance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bruno Lepri, Nicu Sebe, Ruidong Chen, Weijie Wang, Weizhi Nie","submitted_at":"2023-05-25T06:05:52Z","abstract_excerpt":"In recent years, 3D models have been utilized in many applications, such as auto-driver, 3D reconstruction, VR, and AR. However, the scarcity of 3D model data does not meet its practical demands. Thus, generating high-quality 3D models efficiently from textual descriptions is a promising but challenging way to solve this problem. In this paper, inspired by the ability of human beings to complement visual information details from ambiguous descriptions based on their own experience, we propose a novel text-3D generation model (T2TD), which introduces the related shapes or textual information as"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.15753","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/2305.15753/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":"2026-07-05T06:13:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"M+sNgDPl02xz6mckF6zs2ma1rQAd6/P3p3eG0KG9DfhQHlOOsaTZKJKzG9UBbOOlrcmfYvl901Yf71uE8bK/CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T07:08:45.983042Z"},"content_sha256":"7bca6e05dbd0e14b316514a7472948abbde0311497726b944a8904f640a04a9e","schema_version":"1.0","event_id":"sha256:7bca6e05dbd0e14b316514a7472948abbde0311497726b944a8904f640a04a9e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BXNOIXNDFEPKVNLXF3BDSRPHLL/bundle.json","state_url":"https://pith.science/pith/BXNOIXNDFEPKVNLXF3BDSRPHLL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BXNOIXNDFEPKVNLXF3BDSRPHLL/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":"2026-08-07T07:08:45Z","links":{"resolver":"https://pith.science/pith/BXNOIXNDFEPKVNLXF3BDSRPHLL","bundle":"https://pith.science/pith/BXNOIXNDFEPKVNLXF3BDSRPHLL/bundle.json","state":"https://pith.science/pith/BXNOIXNDFEPKVNLXF3BDSRPHLL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BXNOIXNDFEPKVNLXF3BDSRPHLL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:BXNOIXNDFEPKVNLXF3BDSRPHLL","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":"63fb165799311c56f455bb23e361b916366f3a496ac746ffd19c236eef27f389","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-25T06:05:52Z","title_canon_sha256":"41a05fcfc294922b8cd52abd10052a2a456adbcaa1fe1311b0081db8b4d22a76"},"schema_version":"1.0","source":{"id":"2305.15753","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.15753","created_at":"2026-07-05T06:13:44Z"},{"alias_kind":"arxiv_version","alias_value":"2305.15753v1","created_at":"2026-07-05T06:13:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.15753","created_at":"2026-07-05T06:13:44Z"},{"alias_kind":"pith_short_12","alias_value":"BXNOIXNDFEPK","created_at":"2026-07-05T06:13:44Z"},{"alias_kind":"pith_short_16","alias_value":"BXNOIXNDFEPKVNLX","created_at":"2026-07-05T06:13:44Z"},{"alias_kind":"pith_short_8","alias_value":"BXNOIXND","created_at":"2026-07-05T06:13:44Z"}],"graph_snapshots":[{"event_id":"sha256:7bca6e05dbd0e14b316514a7472948abbde0311497726b944a8904f640a04a9e","target":"graph","created_at":"2026-07-05T06:13:44Z","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/2305.15753/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In recent years, 3D models have been utilized in many applications, such as auto-driver, 3D reconstruction, VR, and AR. However, the scarcity of 3D model data does not meet its practical demands. Thus, generating high-quality 3D models efficiently from textual descriptions is a promising but challenging way to solve this problem. In this paper, inspired by the ability of human beings to complement visual information details from ambiguous descriptions based on their own experience, we propose a novel text-3D generation model (T2TD), which introduces the related shapes or textual information as","authors_text":"Bruno Lepri, Nicu Sebe, Ruidong Chen, Weijie Wang, Weizhi Nie","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-25T06:05:52Z","title":"T2TD: Text-3D Generation Model based on Prior Knowledge Guidance"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.15753","kind":"arxiv","version":1},"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":"sha256:7e0c7354b8608cade77d8dfde358147351d7bcb93830e26bb06fc8518cfa5006","target":"record","created_at":"2026-07-05T06:13:44Z","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":"63fb165799311c56f455bb23e361b916366f3a496ac746ffd19c236eef27f389","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-25T06:05:52Z","title_canon_sha256":"41a05fcfc294922b8cd52abd10052a2a456adbcaa1fe1311b0081db8b4d22a76"},"schema_version":"1.0","source":{"id":"2305.15753","kind":"arxiv","version":1}},"canonical_sha256":"0ddae45da3291eaab5772ec23945e75addea3597d95a7e77d3ec3b923d7310e4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0ddae45da3291eaab5772ec23945e75addea3597d95a7e77d3ec3b923d7310e4","first_computed_at":"2026-07-05T06:13:44.312240Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:13:44.312240Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CmSPSMz4xxOywVy39DElJVwn2uIT3gWE5j5Fa8R/z81XzKiigqZdidjlhZZ7+1QFDDA6oLSW6oRg80PGRYEtAg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:13:44.312691Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.15753","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7e0c7354b8608cade77d8dfde358147351d7bcb93830e26bb06fc8518cfa5006","sha256:7bca6e05dbd0e14b316514a7472948abbde0311497726b944a8904f640a04a9e"],"state_sha256":"fddf647afcf87bf663a6a792f1bcf16e5be92bdaec26924458ec385132a6c7fe"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hFwLJ5o+2gUPJ7t1mkIkMIRcEj2dss0OpyMx+EJOAZOwhrAPSYFUt1ZT3g07Ya8nssI+araKM1kqTUmoNN35BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T07:08:45.987220Z","bundle_sha256":"004ac20929e4c357bc9c694a454ababc22188d03acf285bc030b08c241746166"}}