{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:QEVD62HKNBY7T3CBNFKXGIN4YA","short_pith_number":"pith:QEVD62HK","schema_version":"1.0","canonical_sha256":"812a3f68ea6871f9ec4169557321bcc0201c163bfe45b83f6b43dacc8b143e2b","source":{"kind":"arxiv","id":"2112.10759","version":2},"attestation_state":"computed","paper":{"title":"3D-aware Image Synthesis via Learning Structural and Textural Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bolei Zhou, Ceyuan Yang, Sida Peng, Yinghao Xu, Yujun Shen","submitted_at":"2021-12-20T18:59:40Z","abstract_excerpt":"Making generative models 3D-aware bridges the 2D image space and the 3D physical world yet remains challenging. Recent attempts equip a Generative Adversarial Network (GAN) with a Neural Radiance Field (NeRF), which maps 3D coordinates to pixel values, as a 3D prior. However, the implicit function in NeRF has a very local receptive field, making the generator hard to become aware of the global structure. Meanwhile, NeRF is built on volume rendering which can be too costly to produce high-resolution results, increasing the optimization difficulty. To alleviate these two problems, we propose a n"},"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":"2112.10759","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-12-20T18:59:40Z","cross_cats_sorted":[],"title_canon_sha256":"517a6e464961a9527a1f7050ac86e67ab3b90e242161eea3d6840a82d5ef1258","abstract_canon_sha256":"132fae7e3ae3dd80510bb9e1acfc376b451179e81c749e94bbb2591c39dfa341"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:15:12.791517Z","signature_b64":"8LUrdfSG89fQIJser++up2i8viBF9lKneMLGu1biq3VATPNPt6VEVs9xwHSPX7y8aTrYjZtX9yImplfCxscGCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"812a3f68ea6871f9ec4169557321bcc0201c163bfe45b83f6b43dacc8b143e2b","last_reissued_at":"2026-07-05T04:15:12.791038Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:15:12.791038Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"3D-aware Image Synthesis via Learning Structural and Textural Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bolei Zhou, Ceyuan Yang, Sida Peng, Yinghao Xu, Yujun Shen","submitted_at":"2021-12-20T18:59:40Z","abstract_excerpt":"Making generative models 3D-aware bridges the 2D image space and the 3D physical world yet remains challenging. Recent attempts equip a Generative Adversarial Network (GAN) with a Neural Radiance Field (NeRF), which maps 3D coordinates to pixel values, as a 3D prior. However, the implicit function in NeRF has a very local receptive field, making the generator hard to become aware of the global structure. Meanwhile, NeRF is built on volume rendering which can be too costly to produce high-resolution results, increasing the optimization difficulty. To alleviate these two problems, we propose a n"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.10759","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/2112.10759/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":"2112.10759","created_at":"2026-07-05T04:15:12.791095+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.10759v2","created_at":"2026-07-05T04:15:12.791095+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.10759","created_at":"2026-07-05T04:15:12.791095+00:00"},{"alias_kind":"pith_short_12","alias_value":"QEVD62HKNBY7","created_at":"2026-07-05T04:15:12.791095+00:00"},{"alias_kind":"pith_short_16","alias_value":"QEVD62HKNBY7T3CB","created_at":"2026-07-05T04:15:12.791095+00:00"},{"alias_kind":"pith_short_8","alias_value":"QEVD62HK","created_at":"2026-07-05T04:15:12.791095+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.16668","citing_title":"Diffusion Features for Zero-Shot 6DoF Object Pose Estimation","ref_index":61,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QEVD62HKNBY7T3CBNFKXGIN4YA","json":"https://pith.science/pith/QEVD62HKNBY7T3CBNFKXGIN4YA.json","graph_json":"https://pith.science/api/pith-number/QEVD62HKNBY7T3CBNFKXGIN4YA/graph.json","events_json":"https://pith.science/api/pith-number/QEVD62HKNBY7T3CBNFKXGIN4YA/events.json","paper":"https://pith.science/paper/QEVD62HK"},"agent_actions":{"view_html":"https://pith.science/pith/QEVD62HKNBY7T3CBNFKXGIN4YA","download_json":"https://pith.science/pith/QEVD62HKNBY7T3CBNFKXGIN4YA.json","view_paper":"https://pith.science/paper/QEVD62HK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.10759&json=true","fetch_graph":"https://pith.science/api/pith-number/QEVD62HKNBY7T3CBNFKXGIN4YA/graph.json","fetch_events":"https://pith.science/api/pith-number/QEVD62HKNBY7T3CBNFKXGIN4YA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QEVD62HKNBY7T3CBNFKXGIN4YA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QEVD62HKNBY7T3CBNFKXGIN4YA/action/storage_attestation","attest_author":"https://pith.science/pith/QEVD62HKNBY7T3CBNFKXGIN4YA/action/author_attestation","sign_citation":"https://pith.science/pith/QEVD62HKNBY7T3CBNFKXGIN4YA/action/citation_signature","submit_replication":"https://pith.science/pith/QEVD62HKNBY7T3CBNFKXGIN4YA/action/replication_record"}},"created_at":"2026-07-05T04:15:12.791095+00:00","updated_at":"2026-07-05T04:15:12.791095+00:00"}