{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:BZG3JCWJYIPVVQYTXZN2JTRRTH","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"0564991a75a38f59375d28194793f75babbd68abcfa8fef366cc544c6e67acc5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-18T16:52:41Z","title_canon_sha256":"14935b2464f8b7350fafb0d69a2fcf4b6cf390a2f8945475d73c65044e436921"},"schema_version":"1.0","source":{"id":"2605.18645","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.18645","created_at":"2026-05-20T00:06:12Z"},{"alias_kind":"arxiv_version","alias_value":"2605.18645v1","created_at":"2026-05-20T00:06:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.18645","created_at":"2026-05-20T00:06:12Z"},{"alias_kind":"pith_short_12","alias_value":"BZG3JCWJYIPV","created_at":"2026-05-20T00:06:12Z"},{"alias_kind":"pith_short_16","alias_value":"BZG3JCWJYIPVVQYT","created_at":"2026-05-20T00:06:12Z"},{"alias_kind":"pith_short_8","alias_value":"BZG3JCWJ","created_at":"2026-05-20T00:06:12Z"}],"graph_snapshots":[{"event_id":"sha256:da0ef57b211b99413879d794585689eae59bc479e3a53905da7cb03babc1bd2a","target":"graph","created_at":"2026-05-20T00:06:12Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-20T00:01:59.179208Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.18645/integrity.json","findings":[],"snapshot_sha256":"b0da538e85b7c60c4a151b2c8cbf85b9c3c955826e59e7b41129c48bf540453d","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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","authors_text":"Arslan Artykov, Nicol\\'as Violante-Grezzi, Tom Ravaud, Vincent Lepetit","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-18T16:52:41Z","title":"Articulation in Prime: Primitive-Based Articulated Object Understanding from a Single Casual Video"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.18645","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:38480a20b86530a5c1c3b9df6c15038f5722aad05b417a8079e4ee96b8ff5eaa","target":"record","created_at":"2026-05-20T00:06:12Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"0564991a75a38f59375d28194793f75babbd68abcfa8fef366cc544c6e67acc5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-18T16:52:41Z","title_canon_sha256":"14935b2464f8b7350fafb0d69a2fcf4b6cf390a2f8945475d73c65044e436921"},"schema_version":"1.0","source":{"id":"2605.18645","kind":"arxiv","version":1}},"canonical_sha256":"0e4db48ac9c21f5ac313be5ba4ce3199f4bac761b161274cd81706360167b78f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0e4db48ac9c21f5ac313be5ba4ce3199f4bac761b161274cd81706360167b78f","first_computed_at":"2026-05-20T00:06:12.583019Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:06:12.583019Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TFYsqkP/2ZP4JQrsxIjGpkb6WOzkY7o9IHN/n8H0ePgmUiExyJCtKjNAzFQeEb7atyH2DgU27OkGQvgfBsGQDQ==","signature_status":"signed_v1","signed_at":"2026-05-20T00:06:12.583852Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.18645","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:38480a20b86530a5c1c3b9df6c15038f5722aad05b417a8079e4ee96b8ff5eaa","sha256:da0ef57b211b99413879d794585689eae59bc479e3a53905da7cb03babc1bd2a"],"state_sha256":"0759d0f0170ddd887e675941861b729774ffd6ed64846752cff5ba61a837b8e1"}