{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:W635NKAXFN3CEPJOE62SYXCSSW","short_pith_number":"pith:W635NKAX","schema_version":"1.0","canonical_sha256":"b7b7d6a8172b76223d2e27b52c5c5295906b972445c7440255f78db3056a6356","source":{"kind":"arxiv","id":"2505.13215","version":1},"attestation_state":"computed","paper":{"title":"Hybrid 3D-4D Gaussian Splatting for Fast Dynamic Scene Representation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Eunbyung Park, Hyejin Jeon, Seungjun Oh, Younggeun Lee","submitted_at":"2025-05-19T14:59:58Z","abstract_excerpt":"Recent advancements in dynamic 3D scene reconstruction have shown promising results, enabling high-fidelity 3D novel view synthesis with improved temporal consistency. Among these, 4D Gaussian Splatting (4DGS) has emerged as an appealing approach due to its ability to model high-fidelity spatial and temporal variations. However, existing methods suffer from substantial computational and memory overhead due to the redundant allocation of 4D Gaussians to static regions, which can also degrade image quality. In this work, we introduce hybrid 3D-4D Gaussian Splatting (3D-4DGS), a novel framework t"},"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":"2505.13215","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-19T14:59:58Z","cross_cats_sorted":[],"title_canon_sha256":"0184d5d5eb6b0868465cca44a7e2ce72a649be39ddfe547f59a3dcf91b0ee81c","abstract_canon_sha256":"4d725fa6b348649239ffffce384fed17a883d2fd189722dfaa8f76cdc39deaa3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:05:26.948711Z","signature_b64":"v30DPT3xGCX6uGHrdK9MoLeF8jRKDirpwL2P6sMehsFGjgf8mVBvlNoenfBH7/rec5FZIbe9om1lhP6vkdzxCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b7b7d6a8172b76223d2e27b52c5c5295906b972445c7440255f78db3056a6356","last_reissued_at":"2026-07-05T11:05:26.948231Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:05:26.948231Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hybrid 3D-4D Gaussian Splatting for Fast Dynamic Scene Representation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Eunbyung Park, Hyejin Jeon, Seungjun Oh, Younggeun Lee","submitted_at":"2025-05-19T14:59:58Z","abstract_excerpt":"Recent advancements in dynamic 3D scene reconstruction have shown promising results, enabling high-fidelity 3D novel view synthesis with improved temporal consistency. Among these, 4D Gaussian Splatting (4DGS) has emerged as an appealing approach due to its ability to model high-fidelity spatial and temporal variations. However, existing methods suffer from substantial computational and memory overhead due to the redundant allocation of 4D Gaussians to static regions, which can also degrade image quality. In this work, we introduce hybrid 3D-4D Gaussian Splatting (3D-4DGS), a novel framework t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.13215","kind":"arxiv","version":1},"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/2505.13215/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":"2505.13215","created_at":"2026-07-05T11:05:26.948292+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.13215v1","created_at":"2026-07-05T11:05:26.948292+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.13215","created_at":"2026-07-05T11:05:26.948292+00:00"},{"alias_kind":"pith_short_12","alias_value":"W635NKAXFN3C","created_at":"2026-07-05T11:05:26.948292+00:00"},{"alias_kind":"pith_short_16","alias_value":"W635NKAXFN3CEPJO","created_at":"2026-07-05T11:05:26.948292+00:00"},{"alias_kind":"pith_short_8","alias_value":"W635NKAX","created_at":"2026-07-05T11:05:26.948292+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.00157","citing_title":"Progressive Pose-Guided 4D Animal Reconstruction from Monocular Video","ref_index":22,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W635NKAXFN3CEPJOE62SYXCSSW","json":"https://pith.science/pith/W635NKAXFN3CEPJOE62SYXCSSW.json","graph_json":"https://pith.science/api/pith-number/W635NKAXFN3CEPJOE62SYXCSSW/graph.json","events_json":"https://pith.science/api/pith-number/W635NKAXFN3CEPJOE62SYXCSSW/events.json","paper":"https://pith.science/paper/W635NKAX"},"agent_actions":{"view_html":"https://pith.science/pith/W635NKAXFN3CEPJOE62SYXCSSW","download_json":"https://pith.science/pith/W635NKAXFN3CEPJOE62SYXCSSW.json","view_paper":"https://pith.science/paper/W635NKAX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.13215&json=true","fetch_graph":"https://pith.science/api/pith-number/W635NKAXFN3CEPJOE62SYXCSSW/graph.json","fetch_events":"https://pith.science/api/pith-number/W635NKAXFN3CEPJOE62SYXCSSW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W635NKAXFN3CEPJOE62SYXCSSW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W635NKAXFN3CEPJOE62SYXCSSW/action/storage_attestation","attest_author":"https://pith.science/pith/W635NKAXFN3CEPJOE62SYXCSSW/action/author_attestation","sign_citation":"https://pith.science/pith/W635NKAXFN3CEPJOE62SYXCSSW/action/citation_signature","submit_replication":"https://pith.science/pith/W635NKAXFN3CEPJOE62SYXCSSW/action/replication_record"}},"created_at":"2026-07-05T11:05:26.948292+00:00","updated_at":"2026-07-05T11:05:26.948292+00:00"}