{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MENUDEPRUF7S75BZMLYJ5FBJVN","short_pith_number":"pith:MENUDEPR","schema_version":"1.0","canonical_sha256":"611b4191f1a17f2ff43962f09e9429ab56354214e635b6b8f01ece0d9db99a43","source":{"kind":"arxiv","id":"2404.10625","version":2},"attestation_state":"computed","paper":{"title":"Gaussian Splatting Decoder for 3D-aware Generative Adversarial Networks","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anna Hilsmann, Arian Beckmann, Florian Barthel, Peter Eisert, Wieland Morgenstern","submitted_at":"2024-04-16T14:48:40Z","abstract_excerpt":"NeRF-based 3D-aware Generative Adversarial Networks (GANs) like EG3D or GIRAFFE have shown very high rendering quality under large representational variety. However, rendering with Neural Radiance Fields poses challenges for 3D applications: First, the significant computational demands of NeRF rendering preclude its use on low-power devices, such as mobiles and VR/AR headsets. Second, implicit representations based on neural networks are difficult to incorporate into explicit 3D scenes, such as VR environments or video games. 3D Gaussian Splatting (3DGS) overcomes these limitations by providin"},"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":"2404.10625","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-04-16T14:48:40Z","cross_cats_sorted":[],"title_canon_sha256":"8a4b48bcd09e94800704c67263110aa36878a2ac53721d6084f36641a97beb3e","abstract_canon_sha256":"c105865133b4742e2102ce4438caac09646028d64b957f70e1d6a1a4163eaba4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:41:33.760350Z","signature_b64":"pFC2grkJa0eljLK57oFAytMWmGKqfec0rpesGju7B7nkWLWC2kVQ6xwii+GyFx4oGNIZOKXDszsfw4EFf6rHCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"611b4191f1a17f2ff43962f09e9429ab56354214e635b6b8f01ece0d9db99a43","last_reissued_at":"2026-07-05T09:41:33.759931Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:41:33.759931Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gaussian Splatting Decoder for 3D-aware Generative Adversarial Networks","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anna Hilsmann, Arian Beckmann, Florian Barthel, Peter Eisert, Wieland Morgenstern","submitted_at":"2024-04-16T14:48:40Z","abstract_excerpt":"NeRF-based 3D-aware Generative Adversarial Networks (GANs) like EG3D or GIRAFFE have shown very high rendering quality under large representational variety. However, rendering with Neural Radiance Fields poses challenges for 3D applications: First, the significant computational demands of NeRF rendering preclude its use on low-power devices, such as mobiles and VR/AR headsets. Second, implicit representations based on neural networks are difficult to incorporate into explicit 3D scenes, such as VR environments or video games. 3D Gaussian Splatting (3DGS) overcomes these limitations by providin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.10625","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/2404.10625/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":"2404.10625","created_at":"2026-07-05T09:41:33.759987+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.10625v2","created_at":"2026-07-05T09:41:33.759987+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.10625","created_at":"2026-07-05T09:41:33.759987+00:00"},{"alias_kind":"pith_short_12","alias_value":"MENUDEPRUF7S","created_at":"2026-07-05T09:41:33.759987+00:00"},{"alias_kind":"pith_short_16","alias_value":"MENUDEPRUF7S75BZ","created_at":"2026-07-05T09:41:33.759987+00:00"},{"alias_kind":"pith_short_8","alias_value":"MENUDEPR","created_at":"2026-07-05T09:41:33.759987+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.20202","citing_title":"RaRa Clipper: A Clipper for Gaussian Splatting Based on Ray Tracer and Rasterizer","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MENUDEPRUF7S75BZMLYJ5FBJVN","json":"https://pith.science/pith/MENUDEPRUF7S75BZMLYJ5FBJVN.json","graph_json":"https://pith.science/api/pith-number/MENUDEPRUF7S75BZMLYJ5FBJVN/graph.json","events_json":"https://pith.science/api/pith-number/MENUDEPRUF7S75BZMLYJ5FBJVN/events.json","paper":"https://pith.science/paper/MENUDEPR"},"agent_actions":{"view_html":"https://pith.science/pith/MENUDEPRUF7S75BZMLYJ5FBJVN","download_json":"https://pith.science/pith/MENUDEPRUF7S75BZMLYJ5FBJVN.json","view_paper":"https://pith.science/paper/MENUDEPR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.10625&json=true","fetch_graph":"https://pith.science/api/pith-number/MENUDEPRUF7S75BZMLYJ5FBJVN/graph.json","fetch_events":"https://pith.science/api/pith-number/MENUDEPRUF7S75BZMLYJ5FBJVN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MENUDEPRUF7S75BZMLYJ5FBJVN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MENUDEPRUF7S75BZMLYJ5FBJVN/action/storage_attestation","attest_author":"https://pith.science/pith/MENUDEPRUF7S75BZMLYJ5FBJVN/action/author_attestation","sign_citation":"https://pith.science/pith/MENUDEPRUF7S75BZMLYJ5FBJVN/action/citation_signature","submit_replication":"https://pith.science/pith/MENUDEPRUF7S75BZMLYJ5FBJVN/action/replication_record"}},"created_at":"2026-07-05T09:41:33.759987+00:00","updated_at":"2026-07-05T09:41:33.759987+00:00"}