{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:BZG3JCWJYIPVVQYTXZN2JTRRTH","short_pith_number":"pith:BZG3JCWJ","schema_version":"1.0","canonical_sha256":"0e4db48ac9c21f5ac313be5ba4ce3199f4bac761b161274cd81706360167b78f","source":{"kind":"arxiv","id":"2605.18645","version":1},"attestation_state":"computed","paper":{"title":"Articulation in Prime: Primitive-Based Articulated Object Understanding from a Single Casual Video","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Arslan Artykov, Nicol\\'as Violante-Grezzi, Tom Ravaud, Vincent Lepetit","submitted_at":"2026-05-18T16:52:41Z","abstract_excerpt":"Retrieving the 3D kinematics of articulated objects from monocular video is a fundamental challenge in computer vision. Existing methods rely on complex video setups or cues such as long-term point tracking or wide-baseline matching, but are frequently brittle under severe occlusions, rapid camera ego-motion, or weak local features. Learning-based methods, meanwhile, struggle to generalize beyond their training categories. We propose a category-agnostic optimization framework that treats articulated object understanding as a primitive-fitting problem. Geometric primitives serve as a proxy repr"},"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":"2605.18645","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-18T16:52:41Z","cross_cats_sorted":[],"title_canon_sha256":"14935b2464f8b7350fafb0d69a2fcf4b6cf390a2f8945475d73c65044e436921","abstract_canon_sha256":"0564991a75a38f59375d28194793f75babbd68abcfa8fef366cc544c6e67acc5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:06:12.583852Z","signature_b64":"TFYsqkP/2ZP4JQrsxIjGpkb6WOzkY7o9IHN/n8H0ePgmUiExyJCtKjNAzFQeEb7atyH2DgU27OkGQvgfBsGQDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0e4db48ac9c21f5ac313be5ba4ce3199f4bac761b161274cd81706360167b78f","last_reissued_at":"2026-05-20T00:06:12.583019Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:06:12.583019Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Articulation in Prime: Primitive-Based Articulated Object Understanding from a Single Casual Video","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Arslan Artykov, Nicol\\'as Violante-Grezzi, Tom Ravaud, Vincent Lepetit","submitted_at":"2026-05-18T16:52:41Z","abstract_excerpt":"Retrieving the 3D kinematics of articulated objects from monocular video is a fundamental challenge in computer vision. Existing methods rely on complex video setups or cues such as long-term point tracking or wide-baseline matching, but are frequently brittle under severe occlusions, rapid camera ego-motion, or weak local features. Learning-based methods, meanwhile, struggle to generalize beyond their training categories. We propose a category-agnostic optimization framework that treats articulated object understanding as a primitive-fitting problem. Geometric primitives serve as a proxy repr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.18645","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/2605.18645/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"claim_evidence","ran_at":"2026-05-20T00:01:59.179208Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"b0da538e85b7c60c4a151b2c8cbf85b9c3c955826e59e7b41129c48bf540453d"},"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":"2605.18645","created_at":"2026-05-20T00:06:12.583167+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.18645v1","created_at":"2026-05-20T00:06:12.583167+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.18645","created_at":"2026-05-20T00:06:12.583167+00:00"},{"alias_kind":"pith_short_12","alias_value":"BZG3JCWJYIPV","created_at":"2026-05-20T00:06:12.583167+00:00"},{"alias_kind":"pith_short_16","alias_value":"BZG3JCWJYIPVVQYT","created_at":"2026-05-20T00:06:12.583167+00:00"},{"alias_kind":"pith_short_8","alias_value":"BZG3JCWJ","created_at":"2026-05-20T00:06:12.583167+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/BZG3JCWJYIPVVQYTXZN2JTRRTH","json":"https://pith.science/pith/BZG3JCWJYIPVVQYTXZN2JTRRTH.json","graph_json":"https://pith.science/api/pith-number/BZG3JCWJYIPVVQYTXZN2JTRRTH/graph.json","events_json":"https://pith.science/api/pith-number/BZG3JCWJYIPVVQYTXZN2JTRRTH/events.json","paper":"https://pith.science/paper/BZG3JCWJ"},"agent_actions":{"view_html":"https://pith.science/pith/BZG3JCWJYIPVVQYTXZN2JTRRTH","download_json":"https://pith.science/pith/BZG3JCWJYIPVVQYTXZN2JTRRTH.json","view_paper":"https://pith.science/paper/BZG3JCWJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.18645&json=true","fetch_graph":"https://pith.science/api/pith-number/BZG3JCWJYIPVVQYTXZN2JTRRTH/graph.json","fetch_events":"https://pith.science/api/pith-number/BZG3JCWJYIPVVQYTXZN2JTRRTH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BZG3JCWJYIPVVQYTXZN2JTRRTH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BZG3JCWJYIPVVQYTXZN2JTRRTH/action/storage_attestation","attest_author":"https://pith.science/pith/BZG3JCWJYIPVVQYTXZN2JTRRTH/action/author_attestation","sign_citation":"https://pith.science/pith/BZG3JCWJYIPVVQYTXZN2JTRRTH/action/citation_signature","submit_replication":"https://pith.science/pith/BZG3JCWJYIPVVQYTXZN2JTRRTH/action/replication_record"}},"created_at":"2026-05-20T00:06:12.583167+00:00","updated_at":"2026-05-20T00:06:12.583167+00:00"}