{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:LVB7O35KKXP3E6AABAFMPLBVNY","short_pith_number":"pith:LVB7O35K","schema_version":"1.0","canonical_sha256":"5d43f76faa55dfb27800080ac7ac356e11c6aa6ea151ba6b82bd2ef766528a35","source":{"kind":"arxiv","id":"2404.11669","version":3},"attestation_state":"computed","paper":{"title":"Factorized Motion Fields for Fast Sparse Input Dynamic View Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kapil Choudhary, Nagabhushan Somraj, Rajiv Soundararajan, Sai Harsha Mupparaju","submitted_at":"2024-04-17T18:08:00Z","abstract_excerpt":"Designing a 3D representation of a dynamic scene for fast optimization and rendering is a challenging task. While recent explicit representations enable fast learning and rendering of dynamic radiance fields, they require a dense set of input viewpoints. In this work, we focus on learning a fast representation for dynamic radiance fields with sparse input viewpoints. However, the optimization with sparse input is under-constrained and necessitates the use of motion priors to constrain the learning. Existing fast dynamic scene models do not explicitly model the motion, making them difficult to "},"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":"2404.11669","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-04-17T18:08:00Z","cross_cats_sorted":[],"title_canon_sha256":"ba9de9313a896345ccc459f4f955d46b1ed2a91fe8f29fbf293c4523af4ec3b8","abstract_canon_sha256":"4301726c28a983325467336422a8ad3f31ed6a03db08734d378e985ad278d228"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:11:43.929628Z","signature_b64":"Z0BrNROSdlplYd2fDjhzJLnEAuQZO/120e21buIT8Wy2/pVk0TH36IlJDhGt4Asjbr1nTH/31r15BsSbwgQbDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5d43f76faa55dfb27800080ac7ac356e11c6aa6ea151ba6b82bd2ef766528a35","last_reissued_at":"2026-07-05T08:11:43.929096Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:11:43.929096Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Factorized Motion Fields for Fast Sparse Input Dynamic View Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kapil Choudhary, Nagabhushan Somraj, Rajiv Soundararajan, Sai Harsha Mupparaju","submitted_at":"2024-04-17T18:08:00Z","abstract_excerpt":"Designing a 3D representation of a dynamic scene for fast optimization and rendering is a challenging task. While recent explicit representations enable fast learning and rendering of dynamic radiance fields, they require a dense set of input viewpoints. In this work, we focus on learning a fast representation for dynamic radiance fields with sparse input viewpoints. However, the optimization with sparse input is under-constrained and necessitates the use of motion priors to constrain the learning. Existing fast dynamic scene models do not explicitly model the motion, making them difficult to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.11669","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/2404.11669/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":"2404.11669","created_at":"2026-07-05T08:11:43.929151+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.11669v3","created_at":"2026-07-05T08:11:43.929151+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.11669","created_at":"2026-07-05T08:11:43.929151+00:00"},{"alias_kind":"pith_short_12","alias_value":"LVB7O35KKXP3","created_at":"2026-07-05T08:11:43.929151+00:00"},{"alias_kind":"pith_short_16","alias_value":"LVB7O35KKXP3E6AA","created_at":"2026-07-05T08:11:43.929151+00:00"},{"alias_kind":"pith_short_8","alias_value":"LVB7O35K","created_at":"2026-07-05T08:11:43.929151+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.19141","citing_title":"DASH: 4D Hash Encoding with Self-Supervised Decomposition for Real-Time Dynamic Scene Rendering","ref_index":33,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LVB7O35KKXP3E6AABAFMPLBVNY","json":"https://pith.science/pith/LVB7O35KKXP3E6AABAFMPLBVNY.json","graph_json":"https://pith.science/api/pith-number/LVB7O35KKXP3E6AABAFMPLBVNY/graph.json","events_json":"https://pith.science/api/pith-number/LVB7O35KKXP3E6AABAFMPLBVNY/events.json","paper":"https://pith.science/paper/LVB7O35K"},"agent_actions":{"view_html":"https://pith.science/pith/LVB7O35KKXP3E6AABAFMPLBVNY","download_json":"https://pith.science/pith/LVB7O35KKXP3E6AABAFMPLBVNY.json","view_paper":"https://pith.science/paper/LVB7O35K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.11669&json=true","fetch_graph":"https://pith.science/api/pith-number/LVB7O35KKXP3E6AABAFMPLBVNY/graph.json","fetch_events":"https://pith.science/api/pith-number/LVB7O35KKXP3E6AABAFMPLBVNY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LVB7O35KKXP3E6AABAFMPLBVNY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LVB7O35KKXP3E6AABAFMPLBVNY/action/storage_attestation","attest_author":"https://pith.science/pith/LVB7O35KKXP3E6AABAFMPLBVNY/action/author_attestation","sign_citation":"https://pith.science/pith/LVB7O35KKXP3E6AABAFMPLBVNY/action/citation_signature","submit_replication":"https://pith.science/pith/LVB7O35KKXP3E6AABAFMPLBVNY/action/replication_record"}},"created_at":"2026-07-05T08:11:43.929151+00:00","updated_at":"2026-07-05T08:11:43.929151+00:00"}