{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:MSIUMXPJRNR2RWC5WOLYDJ47FH","short_pith_number":"pith:MSIUMXPJ","schema_version":"1.0","canonical_sha256":"6491465de98b63a8d85db39781a79f29de70260a47aad74b86240ba95941ebc8","source":{"kind":"arxiv","id":"2105.05994","version":1},"attestation_state":"computed","paper":{"title":"Neural Trajectory Fields for Dynamic Novel View Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ben Eckart, Chaoyang Wang, Orazio Gallo, Simon Lucey","submitted_at":"2021-05-12T22:38:30Z","abstract_excerpt":"Recent approaches to render photorealistic views from a limited set of photographs have pushed the boundaries of our interactions with pictures of static scenes. The ability to recreate moments, that is, time-varying sequences, is perhaps an even more interesting scenario, but it remains largely unsolved. We introduce DCT-NeRF, a coordinatebased neural representation for dynamic scenes. DCTNeRF learns smooth and stable trajectories over the input sequence for each point in space. This allows us to enforce consistency between any two frames in the sequence, which results in high quality reconst"},"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":"2105.05994","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-05-12T22:38:30Z","cross_cats_sorted":[],"title_canon_sha256":"cfa1df83ab6ef62ce70fea4eb5f175cb582c12378a8db12c73fdb78c27c124d7","abstract_canon_sha256":"5783c4a55d6fd706f7efe8917a8360b8c6bb17141cb10172bb686db3783e6f3f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:40:02.804001Z","signature_b64":"RhIG26q5kXF3Di2USISKGv0XjrNBni1YfXZaDySf2mox26eJlt2yBs0kgYY9hz4hf75kr1ZT/ZXUCx7Csly+Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6491465de98b63a8d85db39781a79f29de70260a47aad74b86240ba95941ebc8","last_reissued_at":"2026-07-05T02:40:02.803569Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:40:02.803569Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural Trajectory Fields for Dynamic Novel View Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ben Eckart, Chaoyang Wang, Orazio Gallo, Simon Lucey","submitted_at":"2021-05-12T22:38:30Z","abstract_excerpt":"Recent approaches to render photorealistic views from a limited set of photographs have pushed the boundaries of our interactions with pictures of static scenes. The ability to recreate moments, that is, time-varying sequences, is perhaps an even more interesting scenario, but it remains largely unsolved. We introduce DCT-NeRF, a coordinatebased neural representation for dynamic scenes. DCTNeRF learns smooth and stable trajectories over the input sequence for each point in space. This allows us to enforce consistency between any two frames in the sequence, which results in high quality reconst"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.05994","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/2105.05994/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":"2105.05994","created_at":"2026-07-05T02:40:02.803628+00:00"},{"alias_kind":"arxiv_version","alias_value":"2105.05994v1","created_at":"2026-07-05T02:40:02.803628+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.05994","created_at":"2026-07-05T02:40:02.803628+00:00"},{"alias_kind":"pith_short_12","alias_value":"MSIUMXPJRNR2","created_at":"2026-07-05T02:40:02.803628+00:00"},{"alias_kind":"pith_short_16","alias_value":"MSIUMXPJRNR2RWC5","created_at":"2026-07-05T02:40:02.803628+00:00"},{"alias_kind":"pith_short_8","alias_value":"MSIUMXPJ","created_at":"2026-07-05T02:40:02.803628+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.08250","citing_title":"On the Design of Mixture-of-Experts for Dynamic Gaussian Splatting","ref_index":17,"is_internal_anchor":true},{"citing_arxiv_id":"2606.08288","citing_title":"MotionVLA: Injecting Geometric Motion into Vision-Language-Action Model","ref_index":45,"is_internal_anchor":false},{"citing_arxiv_id":"2605.31595","citing_title":"Learning Global Motion with Compact Gaussians for Feed-Forward 4D Reconstruction","ref_index":90,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09299","citing_title":"LagrangianSplats: Divergence-Free Transport of Gaussian Primitives for Fluid Reconstruction","ref_index":129,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MSIUMXPJRNR2RWC5WOLYDJ47FH","json":"https://pith.science/pith/MSIUMXPJRNR2RWC5WOLYDJ47FH.json","graph_json":"https://pith.science/api/pith-number/MSIUMXPJRNR2RWC5WOLYDJ47FH/graph.json","events_json":"https://pith.science/api/pith-number/MSIUMXPJRNR2RWC5WOLYDJ47FH/events.json","paper":"https://pith.science/paper/MSIUMXPJ"},"agent_actions":{"view_html":"https://pith.science/pith/MSIUMXPJRNR2RWC5WOLYDJ47FH","download_json":"https://pith.science/pith/MSIUMXPJRNR2RWC5WOLYDJ47FH.json","view_paper":"https://pith.science/paper/MSIUMXPJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2105.05994&json=true","fetch_graph":"https://pith.science/api/pith-number/MSIUMXPJRNR2RWC5WOLYDJ47FH/graph.json","fetch_events":"https://pith.science/api/pith-number/MSIUMXPJRNR2RWC5WOLYDJ47FH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MSIUMXPJRNR2RWC5WOLYDJ47FH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MSIUMXPJRNR2RWC5WOLYDJ47FH/action/storage_attestation","attest_author":"https://pith.science/pith/MSIUMXPJRNR2RWC5WOLYDJ47FH/action/author_attestation","sign_citation":"https://pith.science/pith/MSIUMXPJRNR2RWC5WOLYDJ47FH/action/citation_signature","submit_replication":"https://pith.science/pith/MSIUMXPJRNR2RWC5WOLYDJ47FH/action/replication_record"}},"created_at":"2026-07-05T02:40:02.803628+00:00","updated_at":"2026-07-05T02:40:02.803628+00:00"}