{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:BWWKZOWWOOKGJZ22BP7KRD2DYV","short_pith_number":"pith:BWWKZOWW","schema_version":"1.0","canonical_sha256":"0dacacbad6739464e75a0bfea88f43c55f193a17573afea8dc4d68f787eb9c25","source":{"kind":"arxiv","id":"2511.03589","version":3},"attestation_state":"computed","paper":{"title":"Human Mesh Modeling for Anny Body","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fabien Baradel, Gr\\'egory Rogez, Gu\\'enol\\'e Fiche, Laura Bravo-S\\'anchez, Matthieu Armando, Philippe Weinzaepfel, Romain Br\\'egier, Thomas Lucas","submitted_at":"2025-11-05T16:10:02Z","abstract_excerpt":"Parametric body models provide the structural basis for many human-centric tasks, yet existing models often rely on costly 3D scans and learned shape spaces that are proprietary and demographically narrow. We introduce Anny, a simple, fully differentiable, and scan-free human body model grounded in anthropometric knowledge from the MakeHuman community. Anny defines a continuous, interpretable shape space, where phenotype parameters (e.g. gender, age, height, weight) control blendshapes spanning a wide range of human forms---across ages (from infants to elders), body types, and proportions. Cal"},"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":"2511.03589","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-11-05T16:10:02Z","cross_cats_sorted":[],"title_canon_sha256":"d471757a3e9f47ea2e35f1a0810f14756fd299e8b4311e6bf8889fd2eab28e76","abstract_canon_sha256":"ec83125195d4db89757a4c6fe17372ea3498c92e2fac2de2994a073a5820bb9f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0dacacbad6739464e75a0bfea88f43c55f193a17573afea8dc4d68f787eb9c25","last_reissued_at":"2026-07-31T00:10:10.496620Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-31T00:10:10.496620Z"},"graph_snapshot":{"paper":{"title":"Human Mesh Modeling for Anny Body","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fabien Baradel, Gr\\'egory Rogez, Gu\\'enol\\'e Fiche, Laura Bravo-S\\'anchez, Matthieu Armando, Philippe Weinzaepfel, Romain Br\\'egier, Thomas Lucas","submitted_at":"2025-11-05T16:10:02Z","abstract_excerpt":"Parametric body models provide the structural basis for many human-centric tasks, yet existing models often rely on costly 3D scans and learned shape spaces that are proprietary and demographically narrow. We introduce Anny, a simple, fully differentiable, and scan-free human body model grounded in anthropometric knowledge from the MakeHuman community. Anny defines a continuous, interpretable shape space, where phenotype parameters (e.g. gender, age, height, weight) control blendshapes spanning a wide range of human forms---across ages (from infants to elders), body types, and proportions. Cal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.03589","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/2511.03589/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":"2511.03589","created_at":"2026-07-31T00:10:10.498942+00:00"},{"alias_kind":"arxiv_version","alias_value":"2511.03589v3","created_at":"2026-07-31T00:10:10.498942+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.03589","created_at":"2026-07-31T00:10:10.498942+00:00"},{"alias_kind":"pith_short_12","alias_value":"BWWKZOWWOOKG","created_at":"2026-07-31T00:10:10.498942+00:00"},{"alias_kind":"pith_short_16","alias_value":"BWWKZOWWOOKGJZ22","created_at":"2026-07-31T00:10:10.498942+00:00"},{"alias_kind":"pith_short_8","alias_value":"BWWKZOWW","created_at":"2026-07-31T00:10:10.498942+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"2606.13028","citing_title":"Comparing Commercial Depth Sensor Accuracy for Medical Applications","ref_index":66,"is_internal_anchor":true},{"citing_arxiv_id":"2604.28025","citing_title":"ResiHMR: Residual-Limb Aware Single-Image 3D Human Mesh Recovery for Individuals with Limb Loss","ref_index":3,"is_internal_anchor":true},{"citing_arxiv_id":"2605.04728","citing_title":"Anny-Fit: All-Age Human Mesh Recovery","ref_index":7,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BWWKZOWWOOKGJZ22BP7KRD2DYV","json":"https://pith.science/pith/BWWKZOWWOOKGJZ22BP7KRD2DYV.json","graph_json":"https://pith.science/api/pith-number/BWWKZOWWOOKGJZ22BP7KRD2DYV/graph.json","events_json":"https://pith.science/api/pith-number/BWWKZOWWOOKGJZ22BP7KRD2DYV/events.json","paper":"https://pith.science/paper/BWWKZOWW"},"agent_actions":{"view_html":"https://pith.science/pith/BWWKZOWWOOKGJZ22BP7KRD2DYV","download_json":"https://pith.science/pith/BWWKZOWWOOKGJZ22BP7KRD2DYV.json","view_paper":"https://pith.science/paper/BWWKZOWW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2511.03589&json=true","fetch_graph":"https://pith.science/api/pith-number/BWWKZOWWOOKGJZ22BP7KRD2DYV/graph.json","fetch_events":"https://pith.science/api/pith-number/BWWKZOWWOOKGJZ22BP7KRD2DYV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BWWKZOWWOOKGJZ22BP7KRD2DYV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BWWKZOWWOOKGJZ22BP7KRD2DYV/action/storage_attestation","attest_author":"https://pith.science/pith/BWWKZOWWOOKGJZ22BP7KRD2DYV/action/author_attestation","sign_citation":"https://pith.science/pith/BWWKZOWWOOKGJZ22BP7KRD2DYV/action/citation_signature","submit_replication":"https://pith.science/pith/BWWKZOWWOOKGJZ22BP7KRD2DYV/action/replication_record"}},"created_at":"2026-07-31T00:10:10.498942+00:00","updated_at":"2026-07-31T00:10:10.498942+00:00"}