{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:WYT5UWYW3BZ4M4JOKIQYZ2ANWS","short_pith_number":"pith:WYT5UWYW","schema_version":"1.0","canonical_sha256":"b627da5b16d873c6712e52218ce80db48a15ec56c5e8c275ee49b80b96d3d892","source":{"kind":"arxiv","id":"1803.02665","version":4},"attestation_state":"computed","paper":{"title":"A Neural Network Approach to Missing Marker Reconstruction in Human Motion Capture","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Hedvig Kjellstr\\\"om, Jonas Beskow, Taras Kucherenko","submitted_at":"2018-03-07T14:16:59Z","abstract_excerpt":"Optical motion capture systems have become a widely used technology in various fields, such as augmented reality, robotics, movie production, etc. Such systems use a large number of cameras to triangulate the position of optical markers.The marker positions are estimated with high accuracy. However, especially when tracking articulated bodies, a fraction of the markers in each timestep is missing from the reconstruction. In this paper, we propose to use a neural network approach to learn how human motion is temporally and spatially correlated, and reconstruct missing markers positions through "},"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":"1803.02665","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-07T14:16:59Z","cross_cats_sorted":[],"title_canon_sha256":"88137248a1fe9c1399567e6e59ba31620be1aa235762749a845950a68cca726e","abstract_canon_sha256":"721766e2e1e620a43281cda7efe7ee34b7208a455eb56a06b5c1dde7ded183c3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:59.308041Z","signature_b64":"rwH4RlF4aKQ6dujgJPZ4qS/v4jA+6PnhBPQaaQHESlVYomLOEzmqJ7aFv7KhmHBQ4xl8RXqLxyL+EjOfIbBXDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b627da5b16d873c6712e52218ce80db48a15ec56c5e8c275ee49b80b96d3d892","last_reissued_at":"2026-05-18T00:04:59.307555Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:59.307555Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Neural Network Approach to Missing Marker Reconstruction in Human Motion Capture","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Hedvig Kjellstr\\\"om, Jonas Beskow, Taras Kucherenko","submitted_at":"2018-03-07T14:16:59Z","abstract_excerpt":"Optical motion capture systems have become a widely used technology in various fields, such as augmented reality, robotics, movie production, etc. Such systems use a large number of cameras to triangulate the position of optical markers.The marker positions are estimated with high accuracy. However, especially when tracking articulated bodies, a fraction of the markers in each timestep is missing from the reconstruction. In this paper, we propose to use a neural network approach to learn how human motion is temporally and spatially correlated, and reconstruct missing markers positions through "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.02665","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1803.02665","created_at":"2026-05-18T00:04:59.307620+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.02665v4","created_at":"2026-05-18T00:04:59.307620+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.02665","created_at":"2026-05-18T00:04:59.307620+00:00"},{"alias_kind":"pith_short_12","alias_value":"WYT5UWYW3BZ4","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_16","alias_value":"WYT5UWYW3BZ4M4JO","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_8","alias_value":"WYT5UWYW","created_at":"2026-05-18T12:33:01.666342+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WYT5UWYW3BZ4M4JOKIQYZ2ANWS","json":"https://pith.science/pith/WYT5UWYW3BZ4M4JOKIQYZ2ANWS.json","graph_json":"https://pith.science/api/pith-number/WYT5UWYW3BZ4M4JOKIQYZ2ANWS/graph.json","events_json":"https://pith.science/api/pith-number/WYT5UWYW3BZ4M4JOKIQYZ2ANWS/events.json","paper":"https://pith.science/paper/WYT5UWYW"},"agent_actions":{"view_html":"https://pith.science/pith/WYT5UWYW3BZ4M4JOKIQYZ2ANWS","download_json":"https://pith.science/pith/WYT5UWYW3BZ4M4JOKIQYZ2ANWS.json","view_paper":"https://pith.science/paper/WYT5UWYW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.02665&json=true","fetch_graph":"https://pith.science/api/pith-number/WYT5UWYW3BZ4M4JOKIQYZ2ANWS/graph.json","fetch_events":"https://pith.science/api/pith-number/WYT5UWYW3BZ4M4JOKIQYZ2ANWS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WYT5UWYW3BZ4M4JOKIQYZ2ANWS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WYT5UWYW3BZ4M4JOKIQYZ2ANWS/action/storage_attestation","attest_author":"https://pith.science/pith/WYT5UWYW3BZ4M4JOKIQYZ2ANWS/action/author_attestation","sign_citation":"https://pith.science/pith/WYT5UWYW3BZ4M4JOKIQYZ2ANWS/action/citation_signature","submit_replication":"https://pith.science/pith/WYT5UWYW3BZ4M4JOKIQYZ2ANWS/action/replication_record"}},"created_at":"2026-05-18T00:04:59.307620+00:00","updated_at":"2026-05-18T00:04:59.307620+00:00"}