{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:RFUQBESC53MGRFFAXXLP6QVNVC","short_pith_number":"pith:RFUQBESC","schema_version":"1.0","canonical_sha256":"8969009242eed86894a0bdd6ff42ada8bdf1352cafd36ce762b4d1f07804b8e3","source":{"kind":"arxiv","id":"2109.08730","version":2},"attestation_state":"computed","paper":{"title":"Unsupervised View-Invariant Human Posture Representation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bj\\\"orn Ommer, Faegheh Sardari, Majid Mirmehdi","submitted_at":"2021-09-17T19:23:31Z","abstract_excerpt":"Most recent view-invariant action recognition and performance assessment approaches rely on a large amount of annotated 3D skeleton data to extract view-invariant features. However, acquiring 3D skeleton data can be cumbersome, if not impractical, in in-the-wild scenarios. To overcome this problem, we present a novel unsupervised approach that learns to extract view-invariant 3D human pose representation from a 2D image without using 3D joint data. Our model is trained by exploiting the intrinsic view-invariant properties of human pose between simultaneous frames from different viewpoints and "},"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":"2109.08730","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-09-17T19:23:31Z","cross_cats_sorted":[],"title_canon_sha256":"00ea397282f65a846848990127c292cb36885642f035ea7df1f7bbad931a0647","abstract_canon_sha256":"2115140b8bc0a02611fed463a60bb36d24f537e64d2037a4850d239776a0cabc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:40:50.363573Z","signature_b64":"jaQfT0K5HqT1xPdLkWmUE6aZgEDTFJCfOXZQUR+fuPMBRarx8L8rravZ0A2zSDl0apT2E7xi5ORxEG18fQOdBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8969009242eed86894a0bdd6ff42ada8bdf1352cafd36ce762b4d1f07804b8e3","last_reissued_at":"2026-07-05T08:40:50.363002Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:40:50.363002Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unsupervised View-Invariant Human Posture Representation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bj\\\"orn Ommer, Faegheh Sardari, Majid Mirmehdi","submitted_at":"2021-09-17T19:23:31Z","abstract_excerpt":"Most recent view-invariant action recognition and performance assessment approaches rely on a large amount of annotated 3D skeleton data to extract view-invariant features. However, acquiring 3D skeleton data can be cumbersome, if not impractical, in in-the-wild scenarios. To overcome this problem, we present a novel unsupervised approach that learns to extract view-invariant 3D human pose representation from a 2D image without using 3D joint data. Our model is trained by exploiting the intrinsic view-invariant properties of human pose between simultaneous frames from different viewpoints and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.08730","kind":"arxiv","version":2},"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/2109.08730/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":"2109.08730","created_at":"2026-07-05T08:40:50.363059+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.08730v2","created_at":"2026-07-05T08:40:50.363059+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.08730","created_at":"2026-07-05T08:40:50.363059+00:00"},{"alias_kind":"pith_short_12","alias_value":"RFUQBESC53MG","created_at":"2026-07-05T08:40:50.363059+00:00"},{"alias_kind":"pith_short_16","alias_value":"RFUQBESC53MGRFFA","created_at":"2026-07-05T08:40:50.363059+00:00"},{"alias_kind":"pith_short_8","alias_value":"RFUQBESC","created_at":"2026-07-05T08:40:50.363059+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/RFUQBESC53MGRFFAXXLP6QVNVC","json":"https://pith.science/pith/RFUQBESC53MGRFFAXXLP6QVNVC.json","graph_json":"https://pith.science/api/pith-number/RFUQBESC53MGRFFAXXLP6QVNVC/graph.json","events_json":"https://pith.science/api/pith-number/RFUQBESC53MGRFFAXXLP6QVNVC/events.json","paper":"https://pith.science/paper/RFUQBESC"},"agent_actions":{"view_html":"https://pith.science/pith/RFUQBESC53MGRFFAXXLP6QVNVC","download_json":"https://pith.science/pith/RFUQBESC53MGRFFAXXLP6QVNVC.json","view_paper":"https://pith.science/paper/RFUQBESC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.08730&json=true","fetch_graph":"https://pith.science/api/pith-number/RFUQBESC53MGRFFAXXLP6QVNVC/graph.json","fetch_events":"https://pith.science/api/pith-number/RFUQBESC53MGRFFAXXLP6QVNVC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RFUQBESC53MGRFFAXXLP6QVNVC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RFUQBESC53MGRFFAXXLP6QVNVC/action/storage_attestation","attest_author":"https://pith.science/pith/RFUQBESC53MGRFFAXXLP6QVNVC/action/author_attestation","sign_citation":"https://pith.science/pith/RFUQBESC53MGRFFAXXLP6QVNVC/action/citation_signature","submit_replication":"https://pith.science/pith/RFUQBESC53MGRFFAXXLP6QVNVC/action/replication_record"}},"created_at":"2026-07-05T08:40:50.363059+00:00","updated_at":"2026-07-05T08:40:50.363059+00:00"}