{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:3EHMQ2UT6CFAIJ453TXA44TGWZ","short_pith_number":"pith:3EHMQ2UT","schema_version":"1.0","canonical_sha256":"d90ec86a93f08a04279ddcee0e7266b661dd769e334f3c16581f7678417971cd","source":{"kind":"arxiv","id":"2402.17292","version":1},"attestation_state":"computed","paper":{"title":"DivAvatar: Diverse 3D Avatar Generation with a Single Prompt","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Biwen Lei, Chunyan Miao, Kunhao Liu, Miaomiao Cui, Shijian Lu, Weijing Tao, Xuansong Xie","submitted_at":"2024-02-27T08:10:31Z","abstract_excerpt":"Text-to-Avatar generation has recently made significant strides due to advancements in diffusion models. However, most existing work remains constrained by limited diversity, producing avatars with subtle differences in appearance for a given text prompt. We design DivAvatar, a novel framework that generates diverse avatars, empowering 3D creatives with a multitude of distinct and richly varied 3D avatars from a single text prompt. Different from most existing work that exploits scene-specific 3D representations such as NeRF, DivAvatar finetunes a 3D generative model (i.e., EVA3D), allowing di"},"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":"2402.17292","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-02-27T08:10:31Z","cross_cats_sorted":[],"title_canon_sha256":"ab422e3d7da388aac47a316727467337cdd6e1238c4ad406d1131b5d284c3746","abstract_canon_sha256":"373ab1adbcdc9f1b6630602db4e9195d4acbf07c142d0cde0e3ae8c493f17863"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:49:45.101910Z","signature_b64":"k/kFA5Fz+ayZE6tqXy3Bg76fjYDYAPvkFH81Gx7vhjIiyio2T0tQRGP/y7ue0SwZ2JY/W5AZD4eiml5geYS6AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d90ec86a93f08a04279ddcee0e7266b661dd769e334f3c16581f7678417971cd","last_reissued_at":"2026-07-05T07:49:45.101482Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:49:45.101482Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DivAvatar: Diverse 3D Avatar Generation with a Single Prompt","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Biwen Lei, Chunyan Miao, Kunhao Liu, Miaomiao Cui, Shijian Lu, Weijing Tao, Xuansong Xie","submitted_at":"2024-02-27T08:10:31Z","abstract_excerpt":"Text-to-Avatar generation has recently made significant strides due to advancements in diffusion models. However, most existing work remains constrained by limited diversity, producing avatars with subtle differences in appearance for a given text prompt. We design DivAvatar, a novel framework that generates diverse avatars, empowering 3D creatives with a multitude of distinct and richly varied 3D avatars from a single text prompt. Different from most existing work that exploits scene-specific 3D representations such as NeRF, DivAvatar finetunes a 3D generative model (i.e., EVA3D), allowing di"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.17292","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/2402.17292/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":"2402.17292","created_at":"2026-07-05T07:49:45.101540+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.17292v1","created_at":"2026-07-05T07:49:45.101540+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.17292","created_at":"2026-07-05T07:49:45.101540+00:00"},{"alias_kind":"pith_short_12","alias_value":"3EHMQ2UT6CFA","created_at":"2026-07-05T07:49:45.101540+00:00"},{"alias_kind":"pith_short_16","alias_value":"3EHMQ2UT6CFAIJ45","created_at":"2026-07-05T07:49:45.101540+00:00"},{"alias_kind":"pith_short_8","alias_value":"3EHMQ2UT","created_at":"2026-07-05T07:49:45.101540+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/3EHMQ2UT6CFAIJ453TXA44TGWZ","json":"https://pith.science/pith/3EHMQ2UT6CFAIJ453TXA44TGWZ.json","graph_json":"https://pith.science/api/pith-number/3EHMQ2UT6CFAIJ453TXA44TGWZ/graph.json","events_json":"https://pith.science/api/pith-number/3EHMQ2UT6CFAIJ453TXA44TGWZ/events.json","paper":"https://pith.science/paper/3EHMQ2UT"},"agent_actions":{"view_html":"https://pith.science/pith/3EHMQ2UT6CFAIJ453TXA44TGWZ","download_json":"https://pith.science/pith/3EHMQ2UT6CFAIJ453TXA44TGWZ.json","view_paper":"https://pith.science/paper/3EHMQ2UT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.17292&json=true","fetch_graph":"https://pith.science/api/pith-number/3EHMQ2UT6CFAIJ453TXA44TGWZ/graph.json","fetch_events":"https://pith.science/api/pith-number/3EHMQ2UT6CFAIJ453TXA44TGWZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3EHMQ2UT6CFAIJ453TXA44TGWZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3EHMQ2UT6CFAIJ453TXA44TGWZ/action/storage_attestation","attest_author":"https://pith.science/pith/3EHMQ2UT6CFAIJ453TXA44TGWZ/action/author_attestation","sign_citation":"https://pith.science/pith/3EHMQ2UT6CFAIJ453TXA44TGWZ/action/citation_signature","submit_replication":"https://pith.science/pith/3EHMQ2UT6CFAIJ453TXA44TGWZ/action/replication_record"}},"created_at":"2026-07-05T07:49:45.101540+00:00","updated_at":"2026-07-05T07:49:45.101540+00:00"}