{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:PJRCGNRTVK3RB65MK2EI4WBVGE","short_pith_number":"pith:PJRCGNRT","schema_version":"1.0","canonical_sha256":"7a62233633aab710fbac56888e5835313ea32da43357b3b09a71161fc1f52911","source":{"kind":"arxiv","id":"2310.17768","version":1},"attestation_state":"computed","paper":{"title":"A Dataset of Relighted 3D Interacting Hands","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bo Peng, Gyeongsik Moon, Harley Bellan, Jesse Richardson, Julia Buffalini, Kevyn McPhail, Mallorie Mize, Nicholas Rosen, Philippe de Bree, Rohan Joshi, Shubham Garg, Shunsuke Saito, Takaaki Shiratori, Tomas Simon, Weipeng Xu","submitted_at":"2023-10-26T20:26:50Z","abstract_excerpt":"The two-hand interaction is one of the most challenging signals to analyze due to the self-similarity, complicated articulations, and occlusions of hands. Although several datasets have been proposed for the two-hand interaction analysis, all of them do not achieve 1) diverse and realistic image appearances and 2) diverse and large-scale groundtruth (GT) 3D poses at the same time. In this work, we propose Re:InterHand, a dataset of relighted 3D interacting hands that achieve the two goals. To this end, we employ a state-of-the-art hand relighting network with our accurately tracked two-hand 3D"},"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":"2310.17768","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2023-10-26T20:26:50Z","cross_cats_sorted":[],"title_canon_sha256":"c713a06e0f9a297c4539d103c2037f051534fc9e1f865f95abddce6d63fa9465","abstract_canon_sha256":"31b6f5f3a5240f72f7317dd37e2fa52a0fe8e769001b4021ad03b114e141140f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:05:52.313600Z","signature_b64":"QQ+1FBKNT73NLWab+eK9eY2k8bpwsitaC34zFEwOZujBcPGCv9dwnZCOn1+B7FSZcNN1I5Cxs5TpjYdig+apAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a62233633aab710fbac56888e5835313ea32da43357b3b09a71161fc1f52911","last_reissued_at":"2026-07-05T07:05:52.313144Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:05:52.313144Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Dataset of Relighted 3D Interacting Hands","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bo Peng, Gyeongsik Moon, Harley Bellan, Jesse Richardson, Julia Buffalini, Kevyn McPhail, Mallorie Mize, Nicholas Rosen, Philippe de Bree, Rohan Joshi, Shubham Garg, Shunsuke Saito, Takaaki Shiratori, Tomas Simon, Weipeng Xu","submitted_at":"2023-10-26T20:26:50Z","abstract_excerpt":"The two-hand interaction is one of the most challenging signals to analyze due to the self-similarity, complicated articulations, and occlusions of hands. Although several datasets have been proposed for the two-hand interaction analysis, all of them do not achieve 1) diverse and realistic image appearances and 2) diverse and large-scale groundtruth (GT) 3D poses at the same time. In this work, we propose Re:InterHand, a dataset of relighted 3D interacting hands that achieve the two goals. To this end, we employ a state-of-the-art hand relighting network with our accurately tracked two-hand 3D"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.17768","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2310.17768/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":"2310.17768","created_at":"2026-07-05T07:05:52.313196+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.17768v1","created_at":"2026-07-05T07:05:52.313196+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.17768","created_at":"2026-07-05T07:05:52.313196+00:00"},{"alias_kind":"pith_short_12","alias_value":"PJRCGNRTVK3R","created_at":"2026-07-05T07:05:52.313196+00:00"},{"alias_kind":"pith_short_16","alias_value":"PJRCGNRTVK3RB65M","created_at":"2026-07-05T07:05:52.313196+00:00"},{"alias_kind":"pith_short_8","alias_value":"PJRCGNRT","created_at":"2026-07-05T07:05:52.313196+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.05506","citing_title":"MagicHOI: Leveraging 3D Priors for Accurate Hand-object Reconstruction from Short Monocular Video Clips","ref_index":47,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PJRCGNRTVK3RB65MK2EI4WBVGE","json":"https://pith.science/pith/PJRCGNRTVK3RB65MK2EI4WBVGE.json","graph_json":"https://pith.science/api/pith-number/PJRCGNRTVK3RB65MK2EI4WBVGE/graph.json","events_json":"https://pith.science/api/pith-number/PJRCGNRTVK3RB65MK2EI4WBVGE/events.json","paper":"https://pith.science/paper/PJRCGNRT"},"agent_actions":{"view_html":"https://pith.science/pith/PJRCGNRTVK3RB65MK2EI4WBVGE","download_json":"https://pith.science/pith/PJRCGNRTVK3RB65MK2EI4WBVGE.json","view_paper":"https://pith.science/paper/PJRCGNRT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.17768&json=true","fetch_graph":"https://pith.science/api/pith-number/PJRCGNRTVK3RB65MK2EI4WBVGE/graph.json","fetch_events":"https://pith.science/api/pith-number/PJRCGNRTVK3RB65MK2EI4WBVGE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PJRCGNRTVK3RB65MK2EI4WBVGE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PJRCGNRTVK3RB65MK2EI4WBVGE/action/storage_attestation","attest_author":"https://pith.science/pith/PJRCGNRTVK3RB65MK2EI4WBVGE/action/author_attestation","sign_citation":"https://pith.science/pith/PJRCGNRTVK3RB65MK2EI4WBVGE/action/citation_signature","submit_replication":"https://pith.science/pith/PJRCGNRTVK3RB65MK2EI4WBVGE/action/replication_record"}},"created_at":"2026-07-05T07:05:52.313196+00:00","updated_at":"2026-07-05T07:05:52.313196+00:00"}