{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:YBGMJ4LFPFTVCH2RBPSIBTBWTH","short_pith_number":"pith:YBGMJ4LF","schema_version":"1.0","canonical_sha256":"c04cc4f1657967511f510be480cc3699e819f98cdf004b718e35c70b352682d0","source":{"kind":"arxiv","id":"1902.09868","version":2},"attestation_state":"computed","paper":{"title":"RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bastian Wandt, Bodo Rosenhahn","submitted_at":"2019-02-26T11:23:54Z","abstract_excerpt":"This paper addresses the problem of 3D human pose estimation from single images. While for a long time human skeletons were parameterized and fitted to the observation by satisfying a reprojection error, nowadays researchers directly use neural networks to infer the 3D pose from the observations. However, most of these approaches ignore the fact that a reprojection constraint has to be satisfied and are sensitive to overfitting. We tackle the overfitting problem by ignoring 2D to 3D correspondences. This efficiently avoids a simple memorization of the training data and allows for a weakly supe"},"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":"1902.09868","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-02-26T11:23:54Z","cross_cats_sorted":[],"title_canon_sha256":"487480253397df320b8e41f9f57450f50256241a591e7989c08e8a70be85c2e4","abstract_canon_sha256":"f599104fc365ae2d91cb3cf762363c4c721a0cedd17000bc998110aa81e9e90a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:29.853917Z","signature_b64":"BjJrCr5iGfp09MgCy661SujI1JVLyrFFnu51NbYTfQVQEETfCOxoQB0qHpZRRvW6LwYDHVMCXV6PMPIegY8FBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c04cc4f1657967511f510be480cc3699e819f98cdf004b718e35c70b352682d0","last_reissued_at":"2026-05-17T23:51:29.853505Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:29.853505Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bastian Wandt, Bodo Rosenhahn","submitted_at":"2019-02-26T11:23:54Z","abstract_excerpt":"This paper addresses the problem of 3D human pose estimation from single images. While for a long time human skeletons were parameterized and fitted to the observation by satisfying a reprojection error, nowadays researchers directly use neural networks to infer the 3D pose from the observations. However, most of these approaches ignore the fact that a reprojection constraint has to be satisfied and are sensitive to overfitting. We tackle the overfitting problem by ignoring 2D to 3D correspondences. This efficiently avoids a simple memorization of the training data and allows for a weakly supe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.09868","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":""},"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":"1902.09868","created_at":"2026-05-17T23:51:29.853559+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.09868v2","created_at":"2026-05-17T23:51:29.853559+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.09868","created_at":"2026-05-17T23:51:29.853559+00:00"},{"alias_kind":"pith_short_12","alias_value":"YBGMJ4LFPFTV","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"YBGMJ4LFPFTVCH2R","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"YBGMJ4LF","created_at":"2026-05-18T12:33:33.725879+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/YBGMJ4LFPFTVCH2RBPSIBTBWTH","json":"https://pith.science/pith/YBGMJ4LFPFTVCH2RBPSIBTBWTH.json","graph_json":"https://pith.science/api/pith-number/YBGMJ4LFPFTVCH2RBPSIBTBWTH/graph.json","events_json":"https://pith.science/api/pith-number/YBGMJ4LFPFTVCH2RBPSIBTBWTH/events.json","paper":"https://pith.science/paper/YBGMJ4LF"},"agent_actions":{"view_html":"https://pith.science/pith/YBGMJ4LFPFTVCH2RBPSIBTBWTH","download_json":"https://pith.science/pith/YBGMJ4LFPFTVCH2RBPSIBTBWTH.json","view_paper":"https://pith.science/paper/YBGMJ4LF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.09868&json=true","fetch_graph":"https://pith.science/api/pith-number/YBGMJ4LFPFTVCH2RBPSIBTBWTH/graph.json","fetch_events":"https://pith.science/api/pith-number/YBGMJ4LFPFTVCH2RBPSIBTBWTH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YBGMJ4LFPFTVCH2RBPSIBTBWTH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YBGMJ4LFPFTVCH2RBPSIBTBWTH/action/storage_attestation","attest_author":"https://pith.science/pith/YBGMJ4LFPFTVCH2RBPSIBTBWTH/action/author_attestation","sign_citation":"https://pith.science/pith/YBGMJ4LFPFTVCH2RBPSIBTBWTH/action/citation_signature","submit_replication":"https://pith.science/pith/YBGMJ4LFPFTVCH2RBPSIBTBWTH/action/replication_record"}},"created_at":"2026-05-17T23:51:29.853559+00:00","updated_at":"2026-05-17T23:51:29.853559+00:00"}