{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:KVODPMXTGMAOEA5OPFZP766OLN","short_pith_number":"pith:KVODPMXT","schema_version":"1.0","canonical_sha256":"555c37b2f33300e203ae7972fffbce5b7e9b3c2c3357b7b0e6cf146cc33bb971","source":{"kind":"arxiv","id":"2201.07412","version":2},"attestation_state":"computed","paper":{"title":"Poseur: Direct Human Pose Regression with Transformers","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anton van den Hengel, Chunhua Shen, Weian Mao, Xinlong Wang, Yongtao Ge, Zhibin Wang, Zhi Tian","submitted_at":"2022-01-19T04:31:57Z","abstract_excerpt":"We propose a direct, regression-based approach to 2D human pose estimation from single images. We formulate the problem as a sequence prediction task, which we solve using a Transformer network. This network directly learns a regression mapping from images to the keypoint coordinates, without resorting to intermediate representations such as heatmaps. This approach avoids much of the complexity associated with heatmap-based approaches. To overcome the feature misalignment issues of previous regression-based methods, we propose an attention mechanism that adaptively attends to the features that"},"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":"2201.07412","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-01-19T04:31:57Z","cross_cats_sorted":[],"title_canon_sha256":"aec427fa66e477dd9ea8535c55ba30ef5deaebc2a01c85a6763bffff814323bd","abstract_canon_sha256":"0a212a15bba376d576804b180c2e45702bb1a3830b0c85133a05f8045a02f2c1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:41:53.620600Z","signature_b64":"ESPyVnOGRWtGFQOhJvQgQasP49IpoRyVI1j34TIsaG6Gp8/FdoFA0le75p1fyCgtrui/nRuIXVxL4c9GSqdADA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"555c37b2f33300e203ae7972fffbce5b7e9b3c2c3357b7b0e6cf146cc33bb971","last_reissued_at":"2026-07-05T04:41:53.620188Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:41:53.620188Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Poseur: Direct Human Pose Regression with Transformers","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anton van den Hengel, Chunhua Shen, Weian Mao, Xinlong Wang, Yongtao Ge, Zhibin Wang, Zhi Tian","submitted_at":"2022-01-19T04:31:57Z","abstract_excerpt":"We propose a direct, regression-based approach to 2D human pose estimation from single images. We formulate the problem as a sequence prediction task, which we solve using a Transformer network. This network directly learns a regression mapping from images to the keypoint coordinates, without resorting to intermediate representations such as heatmaps. This approach avoids much of the complexity associated with heatmap-based approaches. To overcome the feature misalignment issues of previous regression-based methods, we propose an attention mechanism that adaptively attends to the features that"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.07412","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/2201.07412/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":"2201.07412","created_at":"2026-07-05T04:41:53.620242+00:00"},{"alias_kind":"arxiv_version","alias_value":"2201.07412v2","created_at":"2026-07-05T04:41:53.620242+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.07412","created_at":"2026-07-05T04:41:53.620242+00:00"},{"alias_kind":"pith_short_12","alias_value":"KVODPMXTGMAO","created_at":"2026-07-05T04:41:53.620242+00:00"},{"alias_kind":"pith_short_16","alias_value":"KVODPMXTGMAOEA5O","created_at":"2026-07-05T04:41:53.620242+00:00"},{"alias_kind":"pith_short_8","alias_value":"KVODPMXT","created_at":"2026-07-05T04:41:53.620242+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/KVODPMXTGMAOEA5OPFZP766OLN","json":"https://pith.science/pith/KVODPMXTGMAOEA5OPFZP766OLN.json","graph_json":"https://pith.science/api/pith-number/KVODPMXTGMAOEA5OPFZP766OLN/graph.json","events_json":"https://pith.science/api/pith-number/KVODPMXTGMAOEA5OPFZP766OLN/events.json","paper":"https://pith.science/paper/KVODPMXT"},"agent_actions":{"view_html":"https://pith.science/pith/KVODPMXTGMAOEA5OPFZP766OLN","download_json":"https://pith.science/pith/KVODPMXTGMAOEA5OPFZP766OLN.json","view_paper":"https://pith.science/paper/KVODPMXT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2201.07412&json=true","fetch_graph":"https://pith.science/api/pith-number/KVODPMXTGMAOEA5OPFZP766OLN/graph.json","fetch_events":"https://pith.science/api/pith-number/KVODPMXTGMAOEA5OPFZP766OLN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KVODPMXTGMAOEA5OPFZP766OLN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KVODPMXTGMAOEA5OPFZP766OLN/action/storage_attestation","attest_author":"https://pith.science/pith/KVODPMXTGMAOEA5OPFZP766OLN/action/author_attestation","sign_citation":"https://pith.science/pith/KVODPMXTGMAOEA5OPFZP766OLN/action/citation_signature","submit_replication":"https://pith.science/pith/KVODPMXTGMAOEA5OPFZP766OLN/action/replication_record"}},"created_at":"2026-07-05T04:41:53.620242+00:00","updated_at":"2026-07-05T04:41:53.620242+00:00"}