{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:G6DD4UCEJC6XKTMXANEGUCA677","short_pith_number":"pith:G6DD4UCE","schema_version":"1.0","canonical_sha256":"37863e504448bd754d9703486a081efff346d47bf21d26d4ddb114004e398d37","source":{"kind":"arxiv","id":"2206.07255","version":2},"attestation_state":"computed","paper":{"title":"GRAM-HD: 3D-Consistent Image Generation at High Resolution with Generative Radiance Manifolds","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianfeng Xiang, Jiaolong Yang, Xin Tong, Yu Deng","submitted_at":"2022-06-15T02:35:51Z","abstract_excerpt":"Recent works have shown that 3D-aware GANs trained on unstructured single image collections can generate multiview images of novel instances. The key underpinnings to achieve this are a 3D radiance field generator and a volume rendering process. However, existing methods either cannot generate high-resolution images (e.g., up to 256X256) due to the high computation cost of neural volume rendering, or rely on 2D CNNs for image-space upsampling which jeopardizes the 3D consistency across different views. This paper proposes a novel 3D-aware GAN that can generate high resolution images (up to 102"},"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":"2206.07255","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-06-15T02:35:51Z","cross_cats_sorted":[],"title_canon_sha256":"9166cb815c141ebfa5188c84fc4106a48c4349b093f7162028e49ad151809a8c","abstract_canon_sha256":"75bb61cdfe68c168953cd34c32adea2153e0f16c3b3cd7ae1b976ede6a5d5a12"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:59:27.295406Z","signature_b64":"H2M9Z0o1FQnVbgl8kRdb1p3tZbU7WgCMyr7nfL1QMg+kXCl+vUEGUVlYt0k62U5QpAgQK8rDPLhjhxZ/hEKACw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"37863e504448bd754d9703486a081efff346d47bf21d26d4ddb114004e398d37","last_reissued_at":"2026-07-05T06:59:27.294877Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:59:27.294877Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GRAM-HD: 3D-Consistent Image Generation at High Resolution with Generative Radiance Manifolds","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianfeng Xiang, Jiaolong Yang, Xin Tong, Yu Deng","submitted_at":"2022-06-15T02:35:51Z","abstract_excerpt":"Recent works have shown that 3D-aware GANs trained on unstructured single image collections can generate multiview images of novel instances. The key underpinnings to achieve this are a 3D radiance field generator and a volume rendering process. However, existing methods either cannot generate high-resolution images (e.g., up to 256X256) due to the high computation cost of neural volume rendering, or rely on 2D CNNs for image-space upsampling which jeopardizes the 3D consistency across different views. This paper proposes a novel 3D-aware GAN that can generate high resolution images (up to 102"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.07255","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/2206.07255/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":"2206.07255","created_at":"2026-07-05T06:59:27.294941+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.07255v2","created_at":"2026-07-05T06:59:27.294941+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.07255","created_at":"2026-07-05T06:59:27.294941+00:00"},{"alias_kind":"pith_short_12","alias_value":"G6DD4UCEJC6X","created_at":"2026-07-05T06:59:27.294941+00:00"},{"alias_kind":"pith_short_16","alias_value":"G6DD4UCEJC6XKTMX","created_at":"2026-07-05T06:59:27.294941+00:00"},{"alias_kind":"pith_short_8","alias_value":"G6DD4UCE","created_at":"2026-07-05T06:59:27.294941+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.23785","citing_title":"Gaussian Variation Field Diffusion for High-fidelity Video-to-4D Synthesis","ref_index":80,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/G6DD4UCEJC6XKTMXANEGUCA677","json":"https://pith.science/pith/G6DD4UCEJC6XKTMXANEGUCA677.json","graph_json":"https://pith.science/api/pith-number/G6DD4UCEJC6XKTMXANEGUCA677/graph.json","events_json":"https://pith.science/api/pith-number/G6DD4UCEJC6XKTMXANEGUCA677/events.json","paper":"https://pith.science/paper/G6DD4UCE"},"agent_actions":{"view_html":"https://pith.science/pith/G6DD4UCEJC6XKTMXANEGUCA677","download_json":"https://pith.science/pith/G6DD4UCEJC6XKTMXANEGUCA677.json","view_paper":"https://pith.science/paper/G6DD4UCE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.07255&json=true","fetch_graph":"https://pith.science/api/pith-number/G6DD4UCEJC6XKTMXANEGUCA677/graph.json","fetch_events":"https://pith.science/api/pith-number/G6DD4UCEJC6XKTMXANEGUCA677/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G6DD4UCEJC6XKTMXANEGUCA677/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G6DD4UCEJC6XKTMXANEGUCA677/action/storage_attestation","attest_author":"https://pith.science/pith/G6DD4UCEJC6XKTMXANEGUCA677/action/author_attestation","sign_citation":"https://pith.science/pith/G6DD4UCEJC6XKTMXANEGUCA677/action/citation_signature","submit_replication":"https://pith.science/pith/G6DD4UCEJC6XKTMXANEGUCA677/action/replication_record"}},"created_at":"2026-07-05T06:59:27.294941+00:00","updated_at":"2026-07-05T06:59:27.294941+00:00"}