{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:HQOMUZ47LCIZ7NRK42HZFHTT2O","short_pith_number":"pith:HQOMUZ47","schema_version":"1.0","canonical_sha256":"3c1cca679f58919fb62ae68f929e73d3a636cd7e5bebd6245d48410ffffcdfcc","source":{"kind":"arxiv","id":"2311.12897","version":2},"attestation_state":"computed","paper":{"title":"A Compact Dynamic 3D Gaussian Representation for Real-Time Dynamic View Synthesis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.GR","authors_text":"Duc Minh Vo, Hideki Nakayama, Kai Katsumata","submitted_at":"2023-11-21T09:17:14Z","abstract_excerpt":"3D Gaussian Splatting (3DGS) has shown remarkable success in synthesizing novel views given multiple views of a static scene. Yet, 3DGS faces challenges when applied to dynamic scenes because 3D Gaussian parameters need to be updated per timestep, requiring a large amount of memory and at least a dozen observations per timestep. To address these limitations, we present a compact dynamic 3D Gaussian representation that models positions and rotations as functions of time with a few parameter approximations while keeping other properties of 3DGS including scale, color and opacity invariant. Our m"},"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":"2311.12897","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.GR","submitted_at":"2023-11-21T09:17:14Z","cross_cats_sorted":[],"title_canon_sha256":"3a77bc2fbf77451dca513729ed7eb6785d87d28fdff8de51bd06e946cf5348a0","abstract_canon_sha256":"74cdaa23f08ccbcb48cb16d439d848079b32bc8878499aa50b924377ae3a0a30"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:40:10.609199Z","signature_b64":"PXpZbRH0quuQ3ZnNdfmzPLJ4JDuTvl2FpJwR2RROtOmfFqx4ZoHQ/ENAaUBOoWuycu0r4K3LGL+O9GML1ZlvAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3c1cca679f58919fb62ae68f929e73d3a636cd7e5bebd6245d48410ffffcdfcc","last_reissued_at":"2026-07-05T08:40:10.608768Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:40:10.608768Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Compact Dynamic 3D Gaussian Representation for Real-Time Dynamic View Synthesis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.GR","authors_text":"Duc Minh Vo, Hideki Nakayama, Kai Katsumata","submitted_at":"2023-11-21T09:17:14Z","abstract_excerpt":"3D Gaussian Splatting (3DGS) has shown remarkable success in synthesizing novel views given multiple views of a static scene. Yet, 3DGS faces challenges when applied to dynamic scenes because 3D Gaussian parameters need to be updated per timestep, requiring a large amount of memory and at least a dozen observations per timestep. To address these limitations, we present a compact dynamic 3D Gaussian representation that models positions and rotations as functions of time with a few parameter approximations while keeping other properties of 3DGS including scale, color and opacity invariant. Our m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.12897","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/2311.12897/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":"2311.12897","created_at":"2026-07-05T08:40:10.608830+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.12897v2","created_at":"2026-07-05T08:40:10.608830+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.12897","created_at":"2026-07-05T08:40:10.608830+00:00"},{"alias_kind":"pith_short_12","alias_value":"HQOMUZ47LCIZ","created_at":"2026-07-05T08:40:10.608830+00:00"},{"alias_kind":"pith_short_16","alias_value":"HQOMUZ47LCIZ7NRK","created_at":"2026-07-05T08:40:10.608830+00:00"},{"alias_kind":"pith_short_8","alias_value":"HQOMUZ47","created_at":"2026-07-05T08:40:10.608830+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.29976","citing_title":"Learning Efficient 4D Gaussian Representations from Monocular Videos with Flow Splatting","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2606.00452","citing_title":"Beyond Static Gaussians: An Empirical Investigation of Architectural Paradigms for Dynamic 3D Scene Reconstruction","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2401.03890","citing_title":"A Survey on 3D Gaussian Splatting","ref_index":94,"is_internal_anchor":false},{"citing_arxiv_id":"2605.11427","citing_title":"PD-4DGS:Progressive Decomposition of 4D Gaussian Splatting for Bandwidth-Adaptive Dynamic Scene Streaming","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08370","citing_title":"SurfelSplat: Learning Efficient and Generalizable Gaussian Surfel Representations for Sparse-View Surface Reconstruction","ref_index":58,"is_internal_anchor":false},{"citing_arxiv_id":"2604.09473","citing_title":"Realizing Immersive Volumetric Video: A Multimodal Framework for 6-DoF VR Engagement","ref_index":22,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HQOMUZ47LCIZ7NRK42HZFHTT2O","json":"https://pith.science/pith/HQOMUZ47LCIZ7NRK42HZFHTT2O.json","graph_json":"https://pith.science/api/pith-number/HQOMUZ47LCIZ7NRK42HZFHTT2O/graph.json","events_json":"https://pith.science/api/pith-number/HQOMUZ47LCIZ7NRK42HZFHTT2O/events.json","paper":"https://pith.science/paper/HQOMUZ47"},"agent_actions":{"view_html":"https://pith.science/pith/HQOMUZ47LCIZ7NRK42HZFHTT2O","download_json":"https://pith.science/pith/HQOMUZ47LCIZ7NRK42HZFHTT2O.json","view_paper":"https://pith.science/paper/HQOMUZ47","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.12897&json=true","fetch_graph":"https://pith.science/api/pith-number/HQOMUZ47LCIZ7NRK42HZFHTT2O/graph.json","fetch_events":"https://pith.science/api/pith-number/HQOMUZ47LCIZ7NRK42HZFHTT2O/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HQOMUZ47LCIZ7NRK42HZFHTT2O/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HQOMUZ47LCIZ7NRK42HZFHTT2O/action/storage_attestation","attest_author":"https://pith.science/pith/HQOMUZ47LCIZ7NRK42HZFHTT2O/action/author_attestation","sign_citation":"https://pith.science/pith/HQOMUZ47LCIZ7NRK42HZFHTT2O/action/citation_signature","submit_replication":"https://pith.science/pith/HQOMUZ47LCIZ7NRK42HZFHTT2O/action/replication_record"}},"created_at":"2026-07-05T08:40:10.608830+00:00","updated_at":"2026-07-05T08:40:10.608830+00:00"}