{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:WJ7ZI6ZQ2XLTZU6ULGN6AF6O4D","short_pith_number":"pith:WJ7ZI6ZQ","schema_version":"1.0","canonical_sha256":"b27f947b30d5d73cd3d4599be017cee0e86c06ab10c66345f3a7652513d5d694","source":{"kind":"arxiv","id":"2312.14937","version":3},"attestation_state":"computed","paper":{"title":"SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Xiaojuan Qi, Xiaoyang Lyu, Yang-Tian Sun, Yan-Pei Cao, Yi-Hua Huang, Ziyi Yang","submitted_at":"2023-12-04T11:57:14Z","abstract_excerpt":"Novel view synthesis for dynamic scenes is still a challenging problem in computer vision and graphics. Recently, Gaussian splatting has emerged as a robust technique to represent static scenes and enable high-quality and real-time novel view synthesis. Building upon this technique, we propose a new representation that explicitly decomposes the motion and appearance of dynamic scenes into sparse control points and dense Gaussians, respectively. Our key idea is to use sparse control points, significantly fewer in number than the Gaussians, to learn compact 6 DoF transformation bases, which can "},"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":"2312.14937","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-04T11:57:14Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"684de3a63ae9d60d33163293c68a636bebf8d3e26028e93e89949e59482880dd","abstract_canon_sha256":"4a91c3cae985fee776e72d387e44eb797e58a9c71e910de7e180878c7ddcbe88"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:07:12.635506Z","signature_b64":"DGpM+Y+JcOztR81nfTeuEAJPvZm/LmGL//DwBh56AzQ27ce5iPonZmw3VR8XmOEQKwj0tM4GCC+IEfW5l+74BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b27f947b30d5d73cd3d4599be017cee0e86c06ab10c66345f3a7652513d5d694","last_reissued_at":"2026-07-05T08:07:12.635041Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:07:12.635041Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Xiaojuan Qi, Xiaoyang Lyu, Yang-Tian Sun, Yan-Pei Cao, Yi-Hua Huang, Ziyi Yang","submitted_at":"2023-12-04T11:57:14Z","abstract_excerpt":"Novel view synthesis for dynamic scenes is still a challenging problem in computer vision and graphics. Recently, Gaussian splatting has emerged as a robust technique to represent static scenes and enable high-quality and real-time novel view synthesis. Building upon this technique, we propose a new representation that explicitly decomposes the motion and appearance of dynamic scenes into sparse control points and dense Gaussians, respectively. Our key idea is to use sparse control points, significantly fewer in number than the Gaussians, to learn compact 6 DoF transformation bases, which can "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.14937","kind":"arxiv","version":3},"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/2312.14937/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":"2312.14937","created_at":"2026-07-05T08:07:12.635094+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.14937v3","created_at":"2026-07-05T08:07:12.635094+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.14937","created_at":"2026-07-05T08:07:12.635094+00:00"},{"alias_kind":"pith_short_12","alias_value":"WJ7ZI6ZQ2XLT","created_at":"2026-07-05T08:07:12.635094+00:00"},{"alias_kind":"pith_short_16","alias_value":"WJ7ZI6ZQ2XLTZU6U","created_at":"2026-07-05T08:07:12.635094+00:00"},{"alias_kind":"pith_short_8","alias_value":"WJ7ZI6ZQ","created_at":"2026-07-05T08:07:12.635094+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.03479","citing_title":"PersistGS: Differentiable Physics for Object Permanence in 4D Gaussian Splatting","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2606.31050","citing_title":"Learning Video Dynamics with Predictive Differentiable Rendering","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2606.00444","citing_title":"Real-Time Physics Simulation with Dynamic Mesh-Gaussian Reconstructions","ref_index":7,"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":9,"is_internal_anchor":false},{"citing_arxiv_id":"2405.15491","citing_title":"GSDeformer: Direct, Real-time and Extensible Cage-based Deformation for 3D Gaussian Splatting","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08547","citing_title":"GaussiAnimate: Reconstruct and Rig Animatable Categories with Level of Dynamics","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18047","citing_title":"GS-STVSR: Ultra-Efficient Continuous Spatio-Temporal Video Super-Resolution via 2D Gaussian Splatting","ref_index":47,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WJ7ZI6ZQ2XLTZU6ULGN6AF6O4D","json":"https://pith.science/pith/WJ7ZI6ZQ2XLTZU6ULGN6AF6O4D.json","graph_json":"https://pith.science/api/pith-number/WJ7ZI6ZQ2XLTZU6ULGN6AF6O4D/graph.json","events_json":"https://pith.science/api/pith-number/WJ7ZI6ZQ2XLTZU6ULGN6AF6O4D/events.json","paper":"https://pith.science/paper/WJ7ZI6ZQ"},"agent_actions":{"view_html":"https://pith.science/pith/WJ7ZI6ZQ2XLTZU6ULGN6AF6O4D","download_json":"https://pith.science/pith/WJ7ZI6ZQ2XLTZU6ULGN6AF6O4D.json","view_paper":"https://pith.science/paper/WJ7ZI6ZQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.14937&json=true","fetch_graph":"https://pith.science/api/pith-number/WJ7ZI6ZQ2XLTZU6ULGN6AF6O4D/graph.json","fetch_events":"https://pith.science/api/pith-number/WJ7ZI6ZQ2XLTZU6ULGN6AF6O4D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WJ7ZI6ZQ2XLTZU6ULGN6AF6O4D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WJ7ZI6ZQ2XLTZU6ULGN6AF6O4D/action/storage_attestation","attest_author":"https://pith.science/pith/WJ7ZI6ZQ2XLTZU6ULGN6AF6O4D/action/author_attestation","sign_citation":"https://pith.science/pith/WJ7ZI6ZQ2XLTZU6ULGN6AF6O4D/action/citation_signature","submit_replication":"https://pith.science/pith/WJ7ZI6ZQ2XLTZU6ULGN6AF6O4D/action/replication_record"}},"created_at":"2026-07-05T08:07:12.635094+00:00","updated_at":"2026-07-05T08:07:12.635094+00:00"}