{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:UQWRMK55AFTXZZD2Y7QIJXHVII","short_pith_number":"pith:UQWRMK55","schema_version":"1.0","canonical_sha256":"a42d162bbd01677ce47ac7e084dcf5423f6e8a22cfcfa6e2aa1b8eeaf462d125","source":{"kind":"arxiv","id":"1807.05380","version":1},"attestation_state":"computed","paper":{"title":"3D Hand Pose Estimation using Simulation and Partial-Supervision with a Shared Latent Space","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chee Peng Lim, Ehsan Abbasnejad, Masoud Abdi, Saeid Nahavandi","submitted_at":"2018-07-14T11:26:19Z","abstract_excerpt":"Tremendous amounts of expensive annotated data are a vital ingredient for state-of-the-art 3d hand pose estimation. Therefore, synthetic data has been popularized as annotations are automatically available. However, models trained only with synthetic samples do not generalize to real data, mainly due to the gap between the distribution of synthetic and real data. In this paper, we propose a novel method that seeks to predict the 3d position of the hand using both synthetic and partially-labeled real data. Accordingly, we form a shared latent space between three modalities: synthetic depth imag"},"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":"1807.05380","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-14T11:26:19Z","cross_cats_sorted":[],"title_canon_sha256":"68aac7e9f14939abf407c5a87f849c9374f8e44fdab3df991ad7b606957d12e7","abstract_canon_sha256":"1c9d43979b3de67450cbba6ef6bfa84dc3bb02ce4fe89e6dc5fd154ef588aa95"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:10:43.679364Z","signature_b64":"k7btv0jWW1SaRBe+Sssomfd/GbKVbNMGC5ChhKqzL805Ag72Wi2Zvx7TL7A/jcZBx1gdNarltvM/AinJ+yqIDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a42d162bbd01677ce47ac7e084dcf5423f6e8a22cfcfa6e2aa1b8eeaf462d125","last_reissued_at":"2026-05-18T00:10:43.678700Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:10:43.678700Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"3D Hand Pose Estimation using Simulation and Partial-Supervision with a Shared Latent Space","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chee Peng Lim, Ehsan Abbasnejad, Masoud Abdi, Saeid Nahavandi","submitted_at":"2018-07-14T11:26:19Z","abstract_excerpt":"Tremendous amounts of expensive annotated data are a vital ingredient for state-of-the-art 3d hand pose estimation. Therefore, synthetic data has been popularized as annotations are automatically available. However, models trained only with synthetic samples do not generalize to real data, mainly due to the gap between the distribution of synthetic and real data. In this paper, we propose a novel method that seeks to predict the 3d position of the hand using both synthetic and partially-labeled real data. Accordingly, we form a shared latent space between three modalities: synthetic depth imag"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.05380","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":""},"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":"1807.05380","created_at":"2026-05-18T00:10:43.678796+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.05380v1","created_at":"2026-05-18T00:10:43.678796+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.05380","created_at":"2026-05-18T00:10:43.678796+00:00"},{"alias_kind":"pith_short_12","alias_value":"UQWRMK55AFTX","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_16","alias_value":"UQWRMK55AFTXZZD2","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_8","alias_value":"UQWRMK55","created_at":"2026-05-18T12:32:56.356000+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/UQWRMK55AFTXZZD2Y7QIJXHVII","json":"https://pith.science/pith/UQWRMK55AFTXZZD2Y7QIJXHVII.json","graph_json":"https://pith.science/api/pith-number/UQWRMK55AFTXZZD2Y7QIJXHVII/graph.json","events_json":"https://pith.science/api/pith-number/UQWRMK55AFTXZZD2Y7QIJXHVII/events.json","paper":"https://pith.science/paper/UQWRMK55"},"agent_actions":{"view_html":"https://pith.science/pith/UQWRMK55AFTXZZD2Y7QIJXHVII","download_json":"https://pith.science/pith/UQWRMK55AFTXZZD2Y7QIJXHVII.json","view_paper":"https://pith.science/paper/UQWRMK55","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.05380&json=true","fetch_graph":"https://pith.science/api/pith-number/UQWRMK55AFTXZZD2Y7QIJXHVII/graph.json","fetch_events":"https://pith.science/api/pith-number/UQWRMK55AFTXZZD2Y7QIJXHVII/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UQWRMK55AFTXZZD2Y7QIJXHVII/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UQWRMK55AFTXZZD2Y7QIJXHVII/action/storage_attestation","attest_author":"https://pith.science/pith/UQWRMK55AFTXZZD2Y7QIJXHVII/action/author_attestation","sign_citation":"https://pith.science/pith/UQWRMK55AFTXZZD2Y7QIJXHVII/action/citation_signature","submit_replication":"https://pith.science/pith/UQWRMK55AFTXZZD2Y7QIJXHVII/action/replication_record"}},"created_at":"2026-05-18T00:10:43.678796+00:00","updated_at":"2026-05-18T00:10:43.678796+00:00"}