{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:WGCULFBTVR575IJSNFK5AX3GKP","short_pith_number":"pith:WGCULFBT","schema_version":"1.0","canonical_sha256":"b185459433ac7bfea1326955d05f6653e4fd257f552015c0fab363520eb7ff5f","source":{"kind":"arxiv","id":"2004.07788","version":1},"attestation_state":"computed","paper":{"title":"RGBD-Dog: Predicting Canine Pose from RGBD Sensors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Darren Cosker, Kwang In Kim, Martin Parsons, Sinead Kearney, Wenbin Li","submitted_at":"2020-04-16T17:34:45Z","abstract_excerpt":"The automatic extraction of animal \\reb{3D} pose from images without markers is of interest in a range of scientific fields. Most work to date predicts animal pose from RGB images, based on 2D labelling of joint positions. However, due to the difficult nature of obtaining training data, no ground truth dataset of 3D animal motion is available to quantitatively evaluate these approaches. In addition, a lack of 3D animal pose data also makes it difficult to train 3D pose-prediction methods in a similar manner to the popular field of body-pose prediction. In our work, we focus on the problem of 3"},"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":"2004.07788","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-04-16T17:34:45Z","cross_cats_sorted":[],"title_canon_sha256":"f50f3b99725f54ed6b3befecc49fd3dc0bc130cd1fc17443dc3f263c2f1d530a","abstract_canon_sha256":"bceb3a0f794771195b8a7ce6df61d26282942f9368442e864d1bdcd8bdad1213"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:55:36.415817Z","signature_b64":"tWf/QBmUPNOaihTP3O0xQILkSqINnN9Tn7VGsVNbEf0PkxwQrJ47wrl/xsDto1d8PSLM73KEM3q2xOq3PsG0Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b185459433ac7bfea1326955d05f6653e4fd257f552015c0fab363520eb7ff5f","last_reissued_at":"2026-07-05T00:55:36.415400Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:55:36.415400Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RGBD-Dog: Predicting Canine Pose from RGBD Sensors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Darren Cosker, Kwang In Kim, Martin Parsons, Sinead Kearney, Wenbin Li","submitted_at":"2020-04-16T17:34:45Z","abstract_excerpt":"The automatic extraction of animal \\reb{3D} pose from images without markers is of interest in a range of scientific fields. Most work to date predicts animal pose from RGB images, based on 2D labelling of joint positions. However, due to the difficult nature of obtaining training data, no ground truth dataset of 3D animal motion is available to quantitatively evaluate these approaches. In addition, a lack of 3D animal pose data also makes it difficult to train 3D pose-prediction methods in a similar manner to the popular field of body-pose prediction. In our work, we focus on the problem of 3"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.07788","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/2004.07788/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":"2004.07788","created_at":"2026-07-05T00:55:36.415456+00:00"},{"alias_kind":"arxiv_version","alias_value":"2004.07788v1","created_at":"2026-07-05T00:55:36.415456+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.07788","created_at":"2026-07-05T00:55:36.415456+00:00"},{"alias_kind":"pith_short_12","alias_value":"WGCULFBTVR57","created_at":"2026-07-05T00:55:36.415456+00:00"},{"alias_kind":"pith_short_16","alias_value":"WGCULFBTVR575IJS","created_at":"2026-07-05T00:55:36.415456+00:00"},{"alias_kind":"pith_short_8","alias_value":"WGCULFBT","created_at":"2026-07-05T00:55:36.415456+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/WGCULFBTVR575IJSNFK5AX3GKP","json":"https://pith.science/pith/WGCULFBTVR575IJSNFK5AX3GKP.json","graph_json":"https://pith.science/api/pith-number/WGCULFBTVR575IJSNFK5AX3GKP/graph.json","events_json":"https://pith.science/api/pith-number/WGCULFBTVR575IJSNFK5AX3GKP/events.json","paper":"https://pith.science/paper/WGCULFBT"},"agent_actions":{"view_html":"https://pith.science/pith/WGCULFBTVR575IJSNFK5AX3GKP","download_json":"https://pith.science/pith/WGCULFBTVR575IJSNFK5AX3GKP.json","view_paper":"https://pith.science/paper/WGCULFBT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2004.07788&json=true","fetch_graph":"https://pith.science/api/pith-number/WGCULFBTVR575IJSNFK5AX3GKP/graph.json","fetch_events":"https://pith.science/api/pith-number/WGCULFBTVR575IJSNFK5AX3GKP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WGCULFBTVR575IJSNFK5AX3GKP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WGCULFBTVR575IJSNFK5AX3GKP/action/storage_attestation","attest_author":"https://pith.science/pith/WGCULFBTVR575IJSNFK5AX3GKP/action/author_attestation","sign_citation":"https://pith.science/pith/WGCULFBTVR575IJSNFK5AX3GKP/action/citation_signature","submit_replication":"https://pith.science/pith/WGCULFBTVR575IJSNFK5AX3GKP/action/replication_record"}},"created_at":"2026-07-05T00:55:36.415456+00:00","updated_at":"2026-07-05T00:55:36.415456+00:00"}