{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:XB5X5FNU22HTK5G6ZE2ZBTT243","short_pith_number":"pith:XB5X5FNU","schema_version":"1.0","canonical_sha256":"b87b7e95b4d68f3574dec93590ce7ae6e62f6fecc6c49606adc96edaee93405b","source":{"kind":"arxiv","id":"1609.09058","version":1},"attestation_state":"computed","paper":{"title":"A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aleix Martinez, Ruiqi Zhao, Yan Wang","submitted_at":"2016-09-28T19:58:37Z","abstract_excerpt":"Three-dimensional shape reconstruction of 2D landmark points on a single image is a hallmark of human vision, but is a task that has been proven difficult for computer vision algorithms. We define a feed-forward deep neural network algorithm that can reconstruct 3D shapes from 2D landmark points almost perfectly (i.e., with extremely small reconstruction errors), even when these 2D landmarks are from a single image. Our experimental results show an improvement of up to two-fold over state-of-the-art computer vision algorithms; 3D shape reconstruction of human faces is given at a reconstruction"},"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":"1609.09058","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-28T19:58:37Z","cross_cats_sorted":[],"title_canon_sha256":"6b9cdf8d424dc580e14f89ce4cb3533ba0b43d1eb74668454c0ba2a3ec0e9e4e","abstract_canon_sha256":"e8858dc3435281a47a1f3cbe7b6bb2930afa522bdefd04042a9e7c5bff1c50e6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:03:41.340019Z","signature_b64":"vIV52vJoZOTh1hoBlCq7frlMKKynfeqns41n263nLapGhlWV0VBMAZs5zThAEB6SbF5eq/8H1MS2vMdDpCFjBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b87b7e95b4d68f3574dec93590ce7ae6e62f6fecc6c49606adc96edaee93405b","last_reissued_at":"2026-05-18T01:03:41.339597Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:03:41.339597Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aleix Martinez, Ruiqi Zhao, Yan Wang","submitted_at":"2016-09-28T19:58:37Z","abstract_excerpt":"Three-dimensional shape reconstruction of 2D landmark points on a single image is a hallmark of human vision, but is a task that has been proven difficult for computer vision algorithms. We define a feed-forward deep neural network algorithm that can reconstruct 3D shapes from 2D landmark points almost perfectly (i.e., with extremely small reconstruction errors), even when these 2D landmarks are from a single image. Our experimental results show an improvement of up to two-fold over state-of-the-art computer vision algorithms; 3D shape reconstruction of human faces is given at a reconstruction"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.09058","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":""},"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":"1609.09058","created_at":"2026-05-18T01:03:41.339661+00:00"},{"alias_kind":"arxiv_version","alias_value":"1609.09058v1","created_at":"2026-05-18T01:03:41.339661+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.09058","created_at":"2026-05-18T01:03:41.339661+00:00"},{"alias_kind":"pith_short_12","alias_value":"XB5X5FNU22HT","created_at":"2026-05-18T12:30:51.357362+00:00"},{"alias_kind":"pith_short_16","alias_value":"XB5X5FNU22HTK5G6","created_at":"2026-05-18T12:30:51.357362+00:00"},{"alias_kind":"pith_short_8","alias_value":"XB5X5FNU","created_at":"2026-05-18T12:30:51.357362+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/XB5X5FNU22HTK5G6ZE2ZBTT243","json":"https://pith.science/pith/XB5X5FNU22HTK5G6ZE2ZBTT243.json","graph_json":"https://pith.science/api/pith-number/XB5X5FNU22HTK5G6ZE2ZBTT243/graph.json","events_json":"https://pith.science/api/pith-number/XB5X5FNU22HTK5G6ZE2ZBTT243/events.json","paper":"https://pith.science/paper/XB5X5FNU"},"agent_actions":{"view_html":"https://pith.science/pith/XB5X5FNU22HTK5G6ZE2ZBTT243","download_json":"https://pith.science/pith/XB5X5FNU22HTK5G6ZE2ZBTT243.json","view_paper":"https://pith.science/paper/XB5X5FNU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1609.09058&json=true","fetch_graph":"https://pith.science/api/pith-number/XB5X5FNU22HTK5G6ZE2ZBTT243/graph.json","fetch_events":"https://pith.science/api/pith-number/XB5X5FNU22HTK5G6ZE2ZBTT243/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XB5X5FNU22HTK5G6ZE2ZBTT243/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XB5X5FNU22HTK5G6ZE2ZBTT243/action/storage_attestation","attest_author":"https://pith.science/pith/XB5X5FNU22HTK5G6ZE2ZBTT243/action/author_attestation","sign_citation":"https://pith.science/pith/XB5X5FNU22HTK5G6ZE2ZBTT243/action/citation_signature","submit_replication":"https://pith.science/pith/XB5X5FNU22HTK5G6ZE2ZBTT243/action/replication_record"}},"created_at":"2026-05-18T01:03:41.339661+00:00","updated_at":"2026-05-18T01:03:41.339661+00:00"}