{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:BQRZZAHHOWTAKPFPO6QJKS365Z","short_pith_number":"pith:BQRZZAHH","schema_version":"1.0","canonical_sha256":"0c239c80e775a6053caf77a0954b7eee5d48c27038951b9cbe30b020559feee6","source":{"kind":"arxiv","id":"1812.05806","version":2},"attestation_state":"computed","paper":{"title":"A Self-Supervised Bootstrap Method for Single-Image 3D Face Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Paulo R. S. Mendonca, Rahul Tewari, Yifan Xing","submitted_at":"2018-12-14T07:46:02Z","abstract_excerpt":"State-of-the-art methods for 3D reconstruction of faces from a single image require 2D-3D pairs of ground-truth data for supervision. Such data is costly to acquire, and most datasets available in the literature are restricted to pairs for which the input 2D images depict faces in a near fronto-parallel pose. Therefore, many data-driven methods for single-image 3D facial reconstruction perform poorly on profile and near-profile faces. We propose a method to improve the performance of single-image 3D facial reconstruction networks by utilizing the network to synthesize its own training data for"},"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":"1812.05806","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-14T07:46:02Z","cross_cats_sorted":[],"title_canon_sha256":"40a97f0f71769a8f6ced6a15f14b73a6e6ca8ea65032f85dd8f36b09f112b9e5","abstract_canon_sha256":"2f2156babd2b6ac51ae7a581a3349b38631e049106e6a4ae51c68833a9f05b32"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:08.309167Z","signature_b64":"qG8ZQWBPwXPcjAm5NpM94SOFtTcrWCH1gAom2fu9w1yitKzPr4pRX51967sJkI/iU5ZoGwkmG1gi1X1YyCSMDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c239c80e775a6053caf77a0954b7eee5d48c27038951b9cbe30b020559feee6","last_reissued_at":"2026-05-17T23:58:08.308548Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:08.308548Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Self-Supervised Bootstrap Method for Single-Image 3D Face Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Paulo R. S. Mendonca, Rahul Tewari, Yifan Xing","submitted_at":"2018-12-14T07:46:02Z","abstract_excerpt":"State-of-the-art methods for 3D reconstruction of faces from a single image require 2D-3D pairs of ground-truth data for supervision. Such data is costly to acquire, and most datasets available in the literature are restricted to pairs for which the input 2D images depict faces in a near fronto-parallel pose. Therefore, many data-driven methods for single-image 3D facial reconstruction perform poorly on profile and near-profile faces. We propose a method to improve the performance of single-image 3D facial reconstruction networks by utilizing the network to synthesize its own training data for"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.05806","kind":"arxiv","version":2},"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":"1812.05806","created_at":"2026-05-17T23:58:08.308642+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.05806v2","created_at":"2026-05-17T23:58:08.308642+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.05806","created_at":"2026-05-17T23:58:08.308642+00:00"},{"alias_kind":"pith_short_12","alias_value":"BQRZZAHHOWTA","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_16","alias_value":"BQRZZAHHOWTAKPFP","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_8","alias_value":"BQRZZAHH","created_at":"2026-05-18T12:32:16.446611+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/BQRZZAHHOWTAKPFPO6QJKS365Z","json":"https://pith.science/pith/BQRZZAHHOWTAKPFPO6QJKS365Z.json","graph_json":"https://pith.science/api/pith-number/BQRZZAHHOWTAKPFPO6QJKS365Z/graph.json","events_json":"https://pith.science/api/pith-number/BQRZZAHHOWTAKPFPO6QJKS365Z/events.json","paper":"https://pith.science/paper/BQRZZAHH"},"agent_actions":{"view_html":"https://pith.science/pith/BQRZZAHHOWTAKPFPO6QJKS365Z","download_json":"https://pith.science/pith/BQRZZAHHOWTAKPFPO6QJKS365Z.json","view_paper":"https://pith.science/paper/BQRZZAHH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.05806&json=true","fetch_graph":"https://pith.science/api/pith-number/BQRZZAHHOWTAKPFPO6QJKS365Z/graph.json","fetch_events":"https://pith.science/api/pith-number/BQRZZAHHOWTAKPFPO6QJKS365Z/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BQRZZAHHOWTAKPFPO6QJKS365Z/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BQRZZAHHOWTAKPFPO6QJKS365Z/action/storage_attestation","attest_author":"https://pith.science/pith/BQRZZAHHOWTAKPFPO6QJKS365Z/action/author_attestation","sign_citation":"https://pith.science/pith/BQRZZAHHOWTAKPFPO6QJKS365Z/action/citation_signature","submit_replication":"https://pith.science/pith/BQRZZAHHOWTAKPFPO6QJKS365Z/action/replication_record"}},"created_at":"2026-05-17T23:58:08.308642+00:00","updated_at":"2026-05-17T23:58:08.308642+00:00"}