{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:SAMZEZEERVD6DYUETXWLEID2MY","short_pith_number":"pith:SAMZEZEE","schema_version":"1.0","canonical_sha256":"90199264848d47e1e2849decb2207a6602f5bd0cff7c891b4507f94a85ec6f7e","source":{"kind":"arxiv","id":"2006.11341","version":1},"attestation_state":"computed","paper":{"title":"Real-time Pupil Tracking from Monocular Video for Digital Puppetry","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrey Vakunov, Artsiom Ablavatski, Ivan Grishchenko, Karthik Raveendran, Matsvei Zhdanovich","submitted_at":"2020-06-19T19:39:32Z","abstract_excerpt":"We present a simple, real-time approach for pupil tracking from live video on mobile devices. Our method extends a state-of-the-art face mesh detector with two new components: a tiny neural network that predicts positions of the pupils in 2D, and a displacement-based estimation of the pupil blend shape coefficients. Our technique can be used to accurately control the pupil movements of a virtual puppet, and lends liveliness and energy to it. The proposed approach runs at over 50 FPS on modern phones, and enables its usage in any real-time puppeteering pipeline."},"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":"2006.11341","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-19T19:39:32Z","cross_cats_sorted":[],"title_canon_sha256":"03c42460cd38f6d824079fe7a272a0040cb3b0d0f48c52ca865a82978a394226","abstract_canon_sha256":"3ee24f640c287dfac10e45aad6b663ae92c65b911032627bfcbc3969a824f943"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:11:49.165986Z","signature_b64":"rPhG7/F0V82Zk6LSYly0WvMtW1iHCsIBSrhbyVj4FQ0wkViNQh6mA6TqF9/g4YkoNS3yK2+4gLmMWVQs2nyvCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"90199264848d47e1e2849decb2207a6602f5bd0cff7c891b4507f94a85ec6f7e","last_reissued_at":"2026-07-05T01:11:49.165519Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:11:49.165519Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Real-time Pupil Tracking from Monocular Video for Digital Puppetry","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrey Vakunov, Artsiom Ablavatski, Ivan Grishchenko, Karthik Raveendran, Matsvei Zhdanovich","submitted_at":"2020-06-19T19:39:32Z","abstract_excerpt":"We present a simple, real-time approach for pupil tracking from live video on mobile devices. Our method extends a state-of-the-art face mesh detector with two new components: a tiny neural network that predicts positions of the pupils in 2D, and a displacement-based estimation of the pupil blend shape coefficients. Our technique can be used to accurately control the pupil movements of a virtual puppet, and lends liveliness and energy to it. The proposed approach runs at over 50 FPS on modern phones, and enables its usage in any real-time puppeteering pipeline."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.11341","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/2006.11341/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":"2006.11341","created_at":"2026-07-05T01:11:49.165575+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.11341v1","created_at":"2026-07-05T01:11:49.165575+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.11341","created_at":"2026-07-05T01:11:49.165575+00:00"},{"alias_kind":"pith_short_12","alias_value":"SAMZEZEERVD6","created_at":"2026-07-05T01:11:49.165575+00:00"},{"alias_kind":"pith_short_16","alias_value":"SAMZEZEERVD6DYUE","created_at":"2026-07-05T01:11:49.165575+00:00"},{"alias_kind":"pith_short_8","alias_value":"SAMZEZEE","created_at":"2026-07-05T01:11:49.165575+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.17158","citing_title":"DOOMGAN:High-Fidelity Dynamic Identity Obfuscation Ocular Generative Morphing","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SAMZEZEERVD6DYUETXWLEID2MY","json":"https://pith.science/pith/SAMZEZEERVD6DYUETXWLEID2MY.json","graph_json":"https://pith.science/api/pith-number/SAMZEZEERVD6DYUETXWLEID2MY/graph.json","events_json":"https://pith.science/api/pith-number/SAMZEZEERVD6DYUETXWLEID2MY/events.json","paper":"https://pith.science/paper/SAMZEZEE"},"agent_actions":{"view_html":"https://pith.science/pith/SAMZEZEERVD6DYUETXWLEID2MY","download_json":"https://pith.science/pith/SAMZEZEERVD6DYUETXWLEID2MY.json","view_paper":"https://pith.science/paper/SAMZEZEE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.11341&json=true","fetch_graph":"https://pith.science/api/pith-number/SAMZEZEERVD6DYUETXWLEID2MY/graph.json","fetch_events":"https://pith.science/api/pith-number/SAMZEZEERVD6DYUETXWLEID2MY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SAMZEZEERVD6DYUETXWLEID2MY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SAMZEZEERVD6DYUETXWLEID2MY/action/storage_attestation","attest_author":"https://pith.science/pith/SAMZEZEERVD6DYUETXWLEID2MY/action/author_attestation","sign_citation":"https://pith.science/pith/SAMZEZEERVD6DYUETXWLEID2MY/action/citation_signature","submit_replication":"https://pith.science/pith/SAMZEZEERVD6DYUETXWLEID2MY/action/replication_record"}},"created_at":"2026-07-05T01:11:49.165575+00:00","updated_at":"2026-07-05T01:11:49.165575+00:00"}