{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:LKEVUM6AS65GG5XISWAJCATAOF","short_pith_number":"pith:LKEVUM6A","canonical_record":{"source":{"id":"2312.03806","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-06T16:23:26Z","cross_cats_sorted":["cs.GR","cs.LG"],"title_canon_sha256":"b9a685a4472af6e9cf06fe206692e4bccd1a379679bbb9c43a004bc0c16d3435","abstract_canon_sha256":"3ed8d004f362158dba4fe9628d21e8ade5963947e59b61137a98800c45ce715d"},"schema_version":"1.0"},"canonical_sha256":"5a895a33c097ba6376e895809102607156ea4dd0fb1fbb19b6b7a94c8ed765c2","source":{"kind":"arxiv","id":"2312.03806","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.03806","created_at":"2026-07-05T08:36:20Z"},{"alias_kind":"arxiv_version","alias_value":"2312.03806v2","created_at":"2026-07-05T08:36:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.03806","created_at":"2026-07-05T08:36:20Z"},{"alias_kind":"pith_short_12","alias_value":"LKEVUM6AS65G","created_at":"2026-07-05T08:36:20Z"},{"alias_kind":"pith_short_16","alias_value":"LKEVUM6AS65GG5XI","created_at":"2026-07-05T08:36:20Z"},{"alias_kind":"pith_short_8","alias_value":"LKEVUM6A","created_at":"2026-07-05T08:36:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:LKEVUM6AS65GG5XISWAJCATAOF","target":"record","payload":{"canonical_record":{"source":{"id":"2312.03806","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-06T16:23:26Z","cross_cats_sorted":["cs.GR","cs.LG"],"title_canon_sha256":"b9a685a4472af6e9cf06fe206692e4bccd1a379679bbb9c43a004bc0c16d3435","abstract_canon_sha256":"3ed8d004f362158dba4fe9628d21e8ade5963947e59b61137a98800c45ce715d"},"schema_version":"1.0"},"canonical_sha256":"5a895a33c097ba6376e895809102607156ea4dd0fb1fbb19b6b7a94c8ed765c2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:36:20.954170Z","signature_b64":"FfkwAxGa1obx8N9+rfKM2Jra7lT4Eg65kBhwCH0hZX42lUdDtiBbaP2b0ssKKdgFmIBXWdXykoSQainpldKODA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5a895a33c097ba6376e895809102607156ea4dd0fb1fbb19b6b7a94c8ed765c2","last_reissued_at":"2026-07-05T08:36:20.953737Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:36:20.953737Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2312.03806","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:36:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"paaYHSwkkG33E+K6pM1jYMFReE9bvk3/GlTSa/pSvCriELWTA5+5DJDjLWwwkGa15Zw43Re1elRENvX+UzgHBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T17:18:19.068078Z"},"content_sha256":"567c69bb84a8239c3c779aa9c5702e2d1d587dfae215e9af698b020ab4805fc0","schema_version":"1.0","event_id":"sha256:567c69bb84a8239c3c779aa9c5702e2d1d587dfae215e9af698b020ab4805fc0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:LKEVUM6AS65GG5XISWAJCATAOF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"XCube: Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchies","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Francis Williams, Jiahui Huang, Ken Museth, Sanja Fidler, Xiaohui Zeng, Xuanchi Ren","submitted_at":"2023-12-06T16:23:26Z","abstract_excerpt":"We present XCube (abbreviated as $\\mathcal{X}^3$), a novel generative model for high-resolution sparse 3D voxel grids with arbitrary attributes. Our model can generate millions of voxels with a finest effective resolution of up to $1024^3$ in a feed-forward fashion without time-consuming test-time optimization. To achieve this, we employ a hierarchical voxel latent diffusion model which generates progressively higher resolution grids in a coarse-to-fine manner using a custom framework built on the highly efficient VDB data structure. Apart from generating high-resolution objects, we demonstrat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.03806","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/2312.03806/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":"2026-07-05T08:36:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/BcqnogVvnQV5TgSTh/AcFeTsdpbum/DszBg1uF6HlwDo9Nznkiiv83WjzvSE8BVGCpfbAdkgLid7FPHvpn2Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T17:18:19.069067Z"},"content_sha256":"d52a55cde05621d71a4cd881a1430ac726cd640a6749976ba3ca4b1c920828cd","schema_version":"1.0","event_id":"sha256:d52a55cde05621d71a4cd881a1430ac726cd640a6749976ba3ca4b1c920828cd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LKEVUM6AS65GG5XISWAJCATAOF/bundle.json","state_url":"https://pith.science/pith/LKEVUM6AS65GG5XISWAJCATAOF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LKEVUM6AS65GG5XISWAJCATAOF/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":"2026-08-06T17:18:19Z","links":{"resolver":"https://pith.science/pith/LKEVUM6AS65GG5XISWAJCATAOF","bundle":"https://pith.science/pith/LKEVUM6AS65GG5XISWAJCATAOF/bundle.json","state":"https://pith.science/pith/LKEVUM6AS65GG5XISWAJCATAOF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LKEVUM6AS65GG5XISWAJCATAOF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:LKEVUM6AS65GG5XISWAJCATAOF","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":"3ed8d004f362158dba4fe9628d21e8ade5963947e59b61137a98800c45ce715d","cross_cats_sorted":["cs.GR","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-06T16:23:26Z","title_canon_sha256":"b9a685a4472af6e9cf06fe206692e4bccd1a379679bbb9c43a004bc0c16d3435"},"schema_version":"1.0","source":{"id":"2312.03806","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.03806","created_at":"2026-07-05T08:36:20Z"},{"alias_kind":"arxiv_version","alias_value":"2312.03806v2","created_at":"2026-07-05T08:36:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.03806","created_at":"2026-07-05T08:36:20Z"},{"alias_kind":"pith_short_12","alias_value":"LKEVUM6AS65G","created_at":"2026-07-05T08:36:20Z"},{"alias_kind":"pith_short_16","alias_value":"LKEVUM6AS65GG5XI","created_at":"2026-07-05T08:36:20Z"},{"alias_kind":"pith_short_8","alias_value":"LKEVUM6A","created_at":"2026-07-05T08:36:20Z"}],"graph_snapshots":[{"event_id":"sha256:d52a55cde05621d71a4cd881a1430ac726cd640a6749976ba3ca4b1c920828cd","target":"graph","created_at":"2026-07-05T08:36:20Z","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/2312.03806/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present XCube (abbreviated as $\\mathcal{X}^3$), a novel generative model for high-resolution sparse 3D voxel grids with arbitrary attributes. Our model can generate millions of voxels with a finest effective resolution of up to $1024^3$ in a feed-forward fashion without time-consuming test-time optimization. To achieve this, we employ a hierarchical voxel latent diffusion model which generates progressively higher resolution grids in a coarse-to-fine manner using a custom framework built on the highly efficient VDB data structure. Apart from generating high-resolution objects, we demonstrat","authors_text":"Francis Williams, Jiahui Huang, Ken Museth, Sanja Fidler, Xiaohui Zeng, Xuanchi Ren","cross_cats":["cs.GR","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-06T16:23:26Z","title":"XCube: Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchies"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.03806","kind":"arxiv","version":2},"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":"sha256:567c69bb84a8239c3c779aa9c5702e2d1d587dfae215e9af698b020ab4805fc0","target":"record","created_at":"2026-07-05T08:36:20Z","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":"3ed8d004f362158dba4fe9628d21e8ade5963947e59b61137a98800c45ce715d","cross_cats_sorted":["cs.GR","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-06T16:23:26Z","title_canon_sha256":"b9a685a4472af6e9cf06fe206692e4bccd1a379679bbb9c43a004bc0c16d3435"},"schema_version":"1.0","source":{"id":"2312.03806","kind":"arxiv","version":2}},"canonical_sha256":"5a895a33c097ba6376e895809102607156ea4dd0fb1fbb19b6b7a94c8ed765c2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5a895a33c097ba6376e895809102607156ea4dd0fb1fbb19b6b7a94c8ed765c2","first_computed_at":"2026-07-05T08:36:20.953737Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:36:20.953737Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FfkwAxGa1obx8N9+rfKM2Jra7lT4Eg65kBhwCH0hZX42lUdDtiBbaP2b0ssKKdgFmIBXWdXykoSQainpldKODA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:36:20.954170Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.03806","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:567c69bb84a8239c3c779aa9c5702e2d1d587dfae215e9af698b020ab4805fc0","sha256:d52a55cde05621d71a4cd881a1430ac726cd640a6749976ba3ca4b1c920828cd"],"state_sha256":"906bb50c05516b395a1dda0f6b25c40af355e398bba31c9929b64fa9eb6a7c05"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rJRYp5kWUY0uRLc6PzM590SzSAIVBDeZoBWgJap18Z71Pyd3tqdS67IIy/C5QrZt7XJsiBjokdtZ9RiW6cadBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T17:18:19.075085Z","bundle_sha256":"fd2ad80e5ba2aa746b3cfd8f86c92525915da6023384aa61fd0c1b37ca0154a4"}}