{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:PRJSOG3NSMSIJHIMRYTYZ2DULG","short_pith_number":"pith:PRJSOG3N","schema_version":"1.0","canonical_sha256":"7c53271b6d9324849d0c8e278ce874598ee72090c9708a42a4ab942fec18fb7f","source":{"kind":"arxiv","id":"2304.01716","version":5},"attestation_state":"computed","paper":{"title":"Decoupling Dynamic Monocular Videos for Dynamic View Synthesis","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Junhui Hou, Meng You","submitted_at":"2023-04-04T11:25:44Z","abstract_excerpt":"The challenge of dynamic view synthesis from dynamic monocular videos, i.e., synthesizing novel views for free viewpoints given a monocular video of a dynamic scene captured by a moving camera, mainly lies in accurately modeling the \\textbf{dynamic objects} of a scene using limited 2D frames, each with a varying timestamp and viewpoint. Existing methods usually require pre-processed 2D optical flow and depth maps by off-the-shelf methods to supervise the network, making them suffer from the inaccuracy of the pre-processed supervision and the ambiguity when lifting the 2D information to 3D. In "},"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":"2304.01716","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2023-04-04T11:25:44Z","cross_cats_sorted":[],"title_canon_sha256":"78e42ea91d2ed8232d0c00a9c8534e6df4bb44a1f82b8151a00ba2df16c0073b","abstract_canon_sha256":"d220deeb60740628983ddbedb046510938e0dc9bf087ac58afa1bf4e862c75e8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:57:33.075593Z","signature_b64":"Yzy42VLX0bOTNCF20v4YfeSRKI8xdpPUg6fbniybpliRbFj+fGeCocyUjQJ0jew/i7adtAtQu5NnVV3YvDxFAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7c53271b6d9324849d0c8e278ce874598ee72090c9708a42a4ab942fec18fb7f","last_reissued_at":"2026-07-05T08:57:33.075129Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:57:33.075129Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Decoupling Dynamic Monocular Videos for Dynamic View Synthesis","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Junhui Hou, Meng You","submitted_at":"2023-04-04T11:25:44Z","abstract_excerpt":"The challenge of dynamic view synthesis from dynamic monocular videos, i.e., synthesizing novel views for free viewpoints given a monocular video of a dynamic scene captured by a moving camera, mainly lies in accurately modeling the \\textbf{dynamic objects} of a scene using limited 2D frames, each with a varying timestamp and viewpoint. Existing methods usually require pre-processed 2D optical flow and depth maps by off-the-shelf methods to supervise the network, making them suffer from the inaccuracy of the pre-processed supervision and the ambiguity when lifting the 2D information to 3D. In "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.01716","kind":"arxiv","version":5},"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/2304.01716/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":"2304.01716","created_at":"2026-07-05T08:57:33.075180+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.01716v5","created_at":"2026-07-05T08:57:33.075180+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.01716","created_at":"2026-07-05T08:57:33.075180+00:00"},{"alias_kind":"pith_short_12","alias_value":"PRJSOG3NSMSI","created_at":"2026-07-05T08:57:33.075180+00:00"},{"alias_kind":"pith_short_16","alias_value":"PRJSOG3NSMSIJHIM","created_at":"2026-07-05T08:57:33.075180+00:00"},{"alias_kind":"pith_short_8","alias_value":"PRJSOG3N","created_at":"2026-07-05T08:57:33.075180+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.09811","citing_title":"TRACE: Learning 3D Gaussian Physical Dynamics from Multi-view Videos","ref_index":58,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PRJSOG3NSMSIJHIMRYTYZ2DULG","json":"https://pith.science/pith/PRJSOG3NSMSIJHIMRYTYZ2DULG.json","graph_json":"https://pith.science/api/pith-number/PRJSOG3NSMSIJHIMRYTYZ2DULG/graph.json","events_json":"https://pith.science/api/pith-number/PRJSOG3NSMSIJHIMRYTYZ2DULG/events.json","paper":"https://pith.science/paper/PRJSOG3N"},"agent_actions":{"view_html":"https://pith.science/pith/PRJSOG3NSMSIJHIMRYTYZ2DULG","download_json":"https://pith.science/pith/PRJSOG3NSMSIJHIMRYTYZ2DULG.json","view_paper":"https://pith.science/paper/PRJSOG3N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.01716&json=true","fetch_graph":"https://pith.science/api/pith-number/PRJSOG3NSMSIJHIMRYTYZ2DULG/graph.json","fetch_events":"https://pith.science/api/pith-number/PRJSOG3NSMSIJHIMRYTYZ2DULG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PRJSOG3NSMSIJHIMRYTYZ2DULG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PRJSOG3NSMSIJHIMRYTYZ2DULG/action/storage_attestation","attest_author":"https://pith.science/pith/PRJSOG3NSMSIJHIMRYTYZ2DULG/action/author_attestation","sign_citation":"https://pith.science/pith/PRJSOG3NSMSIJHIMRYTYZ2DULG/action/citation_signature","submit_replication":"https://pith.science/pith/PRJSOG3NSMSIJHIMRYTYZ2DULG/action/replication_record"}},"created_at":"2026-07-05T08:57:33.075180+00:00","updated_at":"2026-07-05T08:57:33.075180+00:00"}