{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5VRU2M6UTIIW3ID66AFOQRGDEO","short_pith_number":"pith:5VRU2M6U","schema_version":"1.0","canonical_sha256":"ed634d33d49a116da07ef00ae844c323b1f0775c780a3d0230d292cfd954d5a9","source":{"kind":"arxiv","id":"2507.00261","version":1},"attestation_state":"computed","paper":{"title":"VirtualFencer: Generating Fencing Bouts based on Strategies Extracted from In-the-Wild Videos","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"C. Karen Liu, Joao Pedro Araujo, Joy Yun, Purvi Goel, Zhiyin Lin","submitted_at":"2025-06-30T20:55:22Z","abstract_excerpt":"Fencing is a sport where athletes engage in diverse yet strategically logical motions. While most motions fall into a few high-level actions (e.g. step, lunge, parry), the execution can vary widely-fast vs. slow, large vs. small, offensive vs. defensive. Moreover, a fencer's actions are informed by a strategy that often comes in response to the opponent's behavior. This combination of motion diversity with underlying two-player strategy motivates the application of data-driven modeling to fencing. We present VirtualFencer, a system capable of extracting 3D fencing motion and strategy from in-t"},"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":"2507.00261","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-30T20:55:22Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"94ec00f3ca3fed5ace1718df53328bfe66a6da3cb6b5ce14d64cfd2da5f7382d","abstract_canon_sha256":"519498b8037dab33507f8076394fa2c4384d96793d785d55eafda377ff27dcf1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:30:05.254994Z","signature_b64":"QdCUc3r43yHaOxvTtir8OLN6q1fzVgP0bB8k8bn8PkwhP4igytKRGeoPOUhJ06sQXyGRQMvNj1RWLJBe/3QyCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ed634d33d49a116da07ef00ae844c323b1f0775c780a3d0230d292cfd954d5a9","last_reissued_at":"2026-07-05T11:30:05.254376Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:30:05.254376Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"VirtualFencer: Generating Fencing Bouts based on Strategies Extracted from In-the-Wild Videos","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"C. Karen Liu, Joao Pedro Araujo, Joy Yun, Purvi Goel, Zhiyin Lin","submitted_at":"2025-06-30T20:55:22Z","abstract_excerpt":"Fencing is a sport where athletes engage in diverse yet strategically logical motions. While most motions fall into a few high-level actions (e.g. step, lunge, parry), the execution can vary widely-fast vs. slow, large vs. small, offensive vs. defensive. Moreover, a fencer's actions are informed by a strategy that often comes in response to the opponent's behavior. This combination of motion diversity with underlying two-player strategy motivates the application of data-driven modeling to fencing. We present VirtualFencer, a system capable of extracting 3D fencing motion and strategy from in-t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.00261","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/2507.00261/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":"2507.00261","created_at":"2026-07-05T11:30:05.254454+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.00261v1","created_at":"2026-07-05T11:30:05.254454+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.00261","created_at":"2026-07-05T11:30:05.254454+00:00"},{"alias_kind":"pith_short_12","alias_value":"5VRU2M6UTIIW","created_at":"2026-07-05T11:30:05.254454+00:00"},{"alias_kind":"pith_short_16","alias_value":"5VRU2M6UTIIW3ID6","created_at":"2026-07-05T11:30:05.254454+00:00"},{"alias_kind":"pith_short_8","alias_value":"5VRU2M6U","created_at":"2026-07-05T11:30:05.254454+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/5VRU2M6UTIIW3ID66AFOQRGDEO","json":"https://pith.science/pith/5VRU2M6UTIIW3ID66AFOQRGDEO.json","graph_json":"https://pith.science/api/pith-number/5VRU2M6UTIIW3ID66AFOQRGDEO/graph.json","events_json":"https://pith.science/api/pith-number/5VRU2M6UTIIW3ID66AFOQRGDEO/events.json","paper":"https://pith.science/paper/5VRU2M6U"},"agent_actions":{"view_html":"https://pith.science/pith/5VRU2M6UTIIW3ID66AFOQRGDEO","download_json":"https://pith.science/pith/5VRU2M6UTIIW3ID66AFOQRGDEO.json","view_paper":"https://pith.science/paper/5VRU2M6U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.00261&json=true","fetch_graph":"https://pith.science/api/pith-number/5VRU2M6UTIIW3ID66AFOQRGDEO/graph.json","fetch_events":"https://pith.science/api/pith-number/5VRU2M6UTIIW3ID66AFOQRGDEO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5VRU2M6UTIIW3ID66AFOQRGDEO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5VRU2M6UTIIW3ID66AFOQRGDEO/action/storage_attestation","attest_author":"https://pith.science/pith/5VRU2M6UTIIW3ID66AFOQRGDEO/action/author_attestation","sign_citation":"https://pith.science/pith/5VRU2M6UTIIW3ID66AFOQRGDEO/action/citation_signature","submit_replication":"https://pith.science/pith/5VRU2M6UTIIW3ID66AFOQRGDEO/action/replication_record"}},"created_at":"2026-07-05T11:30:05.254454+00:00","updated_at":"2026-07-05T11:30:05.254454+00:00"}