{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:NRT4NSCOQJS6QKSZBM7KESABQO","short_pith_number":"pith:NRT4NSCO","schema_version":"1.0","canonical_sha256":"6c67c6c84e8265e82a590b3ea2480183a7aee90c04a22b4d4f38c7bcd0e4aeeb","source":{"kind":"arxiv","id":"2110.07588","version":3},"attestation_state":"computed","paper":{"title":"Playing for 3D Human Recovery","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Change Loy, Chen Wei, Daxuan Ren, Haiyu Zhao, Jiawei Ren, Lei Yang, Mingyuan Zhang, Zhengyu Lin, Zhongang Cai, Ziwei Liu","submitted_at":"2021-10-14T17:49:42Z","abstract_excerpt":"Image- and video-based 3D human recovery (i.e., pose and shape estimation) have achieved substantial progress. However, due to the prohibitive cost of motion capture, existing datasets are often limited in scale and diversity. In this work, we obtain massive human sequences by playing the video game with automatically annotated 3D ground truths. Specifically, we contribute GTA-Human, a large-scale 3D human dataset generated with the GTA-V game engine, featuring a highly diverse set of subjects, actions, and scenarios. More importantly, we study the use of game-playing data and obtain five majo"},"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":"2110.07588","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-10-14T17:49:42Z","cross_cats_sorted":[],"title_canon_sha256":"7e532c5cd9586e9f6d3fc274ac55ba52333b0e27d4ee9283ffde0b1070988dc0","abstract_canon_sha256":"9859f6b51a00e4b0d5375c3bbfb4b8ca7e6b0176c1a5d1545c0214c8bb29eb63"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:05:07.911496Z","signature_b64":"q41GfBII3AtbZQHezNtNL/lZAzYWFl21QvbP62tZ6k8EA5TSB2aUttg8TYt6LaDY8sRS1R1QuQqmVzh9pvWMDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c67c6c84e8265e82a590b3ea2480183a7aee90c04a22b4d4f38c7bcd0e4aeeb","last_reissued_at":"2026-07-05T09:05:07.911094Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:05:07.911094Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Playing for 3D Human Recovery","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Change Loy, Chen Wei, Daxuan Ren, Haiyu Zhao, Jiawei Ren, Lei Yang, Mingyuan Zhang, Zhengyu Lin, Zhongang Cai, Ziwei Liu","submitted_at":"2021-10-14T17:49:42Z","abstract_excerpt":"Image- and video-based 3D human recovery (i.e., pose and shape estimation) have achieved substantial progress. However, due to the prohibitive cost of motion capture, existing datasets are often limited in scale and diversity. In this work, we obtain massive human sequences by playing the video game with automatically annotated 3D ground truths. Specifically, we contribute GTA-Human, a large-scale 3D human dataset generated with the GTA-V game engine, featuring a highly diverse set of subjects, actions, and scenarios. More importantly, we study the use of game-playing data and obtain five majo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.07588","kind":"arxiv","version":3},"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/2110.07588/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":"2110.07588","created_at":"2026-07-05T09:05:07.911154+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.07588v3","created_at":"2026-07-05T09:05:07.911154+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.07588","created_at":"2026-07-05T09:05:07.911154+00:00"},{"alias_kind":"pith_short_12","alias_value":"NRT4NSCOQJS6","created_at":"2026-07-05T09:05:07.911154+00:00"},{"alias_kind":"pith_short_16","alias_value":"NRT4NSCOQJS6QKSZ","created_at":"2026-07-05T09:05:07.911154+00:00"},{"alias_kind":"pith_short_8","alias_value":"NRT4NSCO","created_at":"2026-07-05T09:05:07.911154+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/NRT4NSCOQJS6QKSZBM7KESABQO","json":"https://pith.science/pith/NRT4NSCOQJS6QKSZBM7KESABQO.json","graph_json":"https://pith.science/api/pith-number/NRT4NSCOQJS6QKSZBM7KESABQO/graph.json","events_json":"https://pith.science/api/pith-number/NRT4NSCOQJS6QKSZBM7KESABQO/events.json","paper":"https://pith.science/paper/NRT4NSCO"},"agent_actions":{"view_html":"https://pith.science/pith/NRT4NSCOQJS6QKSZBM7KESABQO","download_json":"https://pith.science/pith/NRT4NSCOQJS6QKSZBM7KESABQO.json","view_paper":"https://pith.science/paper/NRT4NSCO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.07588&json=true","fetch_graph":"https://pith.science/api/pith-number/NRT4NSCOQJS6QKSZBM7KESABQO/graph.json","fetch_events":"https://pith.science/api/pith-number/NRT4NSCOQJS6QKSZBM7KESABQO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NRT4NSCOQJS6QKSZBM7KESABQO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NRT4NSCOQJS6QKSZBM7KESABQO/action/storage_attestation","attest_author":"https://pith.science/pith/NRT4NSCOQJS6QKSZBM7KESABQO/action/author_attestation","sign_citation":"https://pith.science/pith/NRT4NSCOQJS6QKSZBM7KESABQO/action/citation_signature","submit_replication":"https://pith.science/pith/NRT4NSCOQJS6QKSZBM7KESABQO/action/replication_record"}},"created_at":"2026-07-05T09:05:07.911154+00:00","updated_at":"2026-07-05T09:05:07.911154+00:00"}