{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:SINBZMCLP4DVUU3RUAKJ6BQLWU","short_pith_number":"pith:SINBZMCL","schema_version":"1.0","canonical_sha256":"921a1cb04b7f075a5371a0149f060bb5229bcaee5f42066511f4c5862f4805da","source":{"kind":"arxiv","id":"2312.08291","version":4},"attestation_state":"computed","paper":{"title":"VQ-HPS: Human Pose and Shape Estimation in a Vector-Quantized Latent Space","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Antonio Agudo, Francesc Moreno-Noguer, Gu\\'enol\\'e Fiche, Simon Leglaive, Xavier Alameda-Pineda","submitted_at":"2023-12-13T17:08:38Z","abstract_excerpt":"Previous works on Human Pose and Shape Estimation (HPSE) from RGB images can be broadly categorized into two main groups: parametric and non-parametric approaches. Parametric techniques leverage a low-dimensional statistical body model for realistic results, whereas recent non-parametric methods achieve higher precision by directly regressing the 3D coordinates of the human body mesh. This work introduces a novel paradigm to address the HPSE problem, involving a low-dimensional discrete latent representation of the human mesh and framing HPSE as a classification task. Instead of predicting bod"},"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":"2312.08291","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-13T17:08:38Z","cross_cats_sorted":[],"title_canon_sha256":"4dc0587c4250dd10c619e6fd088ee27ce1f81654fcccba9bd836080ef02b0321","abstract_canon_sha256":"7eabfc4d1ee7e68d18554b6a8756414f4138cf4f70b905f787d05fa324c63e7f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:43:51.927054Z","signature_b64":"Gz91AMtlPwEcL8sJ9MHmXphg47tshqPVWQYJ8KW18ETS1hTbvUZJ4aL+6rvH7aISWRAOoaY6gMm8IHUzufFABQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"921a1cb04b7f075a5371a0149f060bb5229bcaee5f42066511f4c5862f4805da","last_reissued_at":"2026-07-05T08:43:51.926571Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:43:51.926571Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"VQ-HPS: Human Pose and Shape Estimation in a Vector-Quantized Latent Space","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Antonio Agudo, Francesc Moreno-Noguer, Gu\\'enol\\'e Fiche, Simon Leglaive, Xavier Alameda-Pineda","submitted_at":"2023-12-13T17:08:38Z","abstract_excerpt":"Previous works on Human Pose and Shape Estimation (HPSE) from RGB images can be broadly categorized into two main groups: parametric and non-parametric approaches. Parametric techniques leverage a low-dimensional statistical body model for realistic results, whereas recent non-parametric methods achieve higher precision by directly regressing the 3D coordinates of the human body mesh. This work introduces a novel paradigm to address the HPSE problem, involving a low-dimensional discrete latent representation of the human mesh and framing HPSE as a classification task. Instead of predicting bod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.08291","kind":"arxiv","version":4},"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/2312.08291/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":"2312.08291","created_at":"2026-07-05T08:43:51.926630+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.08291v4","created_at":"2026-07-05T08:43:51.926630+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.08291","created_at":"2026-07-05T08:43:51.926630+00:00"},{"alias_kind":"pith_short_12","alias_value":"SINBZMCLP4DV","created_at":"2026-07-05T08:43:51.926630+00:00"},{"alias_kind":"pith_short_16","alias_value":"SINBZMCLP4DVUU3R","created_at":"2026-07-05T08:43:51.926630+00:00"},{"alias_kind":"pith_short_8","alias_value":"SINBZMCL","created_at":"2026-07-05T08:43:51.926630+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.07800","citing_title":"BioPose: Biomechanically-accurate 3D Pose Estimation from Monocular Videos","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SINBZMCLP4DVUU3RUAKJ6BQLWU","json":"https://pith.science/pith/SINBZMCLP4DVUU3RUAKJ6BQLWU.json","graph_json":"https://pith.science/api/pith-number/SINBZMCLP4DVUU3RUAKJ6BQLWU/graph.json","events_json":"https://pith.science/api/pith-number/SINBZMCLP4DVUU3RUAKJ6BQLWU/events.json","paper":"https://pith.science/paper/SINBZMCL"},"agent_actions":{"view_html":"https://pith.science/pith/SINBZMCLP4DVUU3RUAKJ6BQLWU","download_json":"https://pith.science/pith/SINBZMCLP4DVUU3RUAKJ6BQLWU.json","view_paper":"https://pith.science/paper/SINBZMCL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.08291&json=true","fetch_graph":"https://pith.science/api/pith-number/SINBZMCLP4DVUU3RUAKJ6BQLWU/graph.json","fetch_events":"https://pith.science/api/pith-number/SINBZMCLP4DVUU3RUAKJ6BQLWU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SINBZMCLP4DVUU3RUAKJ6BQLWU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SINBZMCLP4DVUU3RUAKJ6BQLWU/action/storage_attestation","attest_author":"https://pith.science/pith/SINBZMCLP4DVUU3RUAKJ6BQLWU/action/author_attestation","sign_citation":"https://pith.science/pith/SINBZMCLP4DVUU3RUAKJ6BQLWU/action/citation_signature","submit_replication":"https://pith.science/pith/SINBZMCLP4DVUU3RUAKJ6BQLWU/action/replication_record"}},"created_at":"2026-07-05T08:43:51.926630+00:00","updated_at":"2026-07-05T08:43:51.926630+00:00"}