{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:SQV7LEWNBXZIZR5VKCN2RW2RCW","short_pith_number":"pith:SQV7LEWN","schema_version":"1.0","canonical_sha256":"942bf592cd0df28cc7b5509ba8db5115bbecc5fe9fd450fd1c90e8ccf75ade5d","source":{"kind":"arxiv","id":"1612.01202","version":2},"attestation_state":"computed","paper":{"title":"DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Epameinondas Antonakos, George Trigeorgis, Iasonas Kokkinos, Patrick Snape, R{\\i}za Alp G\\\"uler, Stefanos Zafeiriou","submitted_at":"2016-12-04T23:08:06Z","abstract_excerpt":"In this paper we propose to learn a mapping from image pixels into a dense template grid through a fully convolutional network. We formulate this task as a regression problem and train our network by leveraging upon manually annotated facial landmarks \"in-the-wild\". We use such landmarks to establish a dense correspondence field between a three-dimensional object template and the input image, which then serves as the ground-truth for training our regression system. We show that we can combine ideas from semantic segmentation with regression networks, yielding a highly-accurate \"quantized regre"},"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":"1612.01202","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-04T23:08:06Z","cross_cats_sorted":[],"title_canon_sha256":"8a7f34fc3371525cb0c7ae7e7ed35851654732d0e7e408b5297f3449bbc2f451","abstract_canon_sha256":"bcee08938c68b65e6e1fde4a9332bc0f415b174d152ec8a20656392353f91210"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:07.363430Z","signature_b64":"qg+O/snPpUEa4sqW08bwO2Vm7cjAIgb9XhmhsYZpZu//8gzEAgHKjH5GGbcL7zjL3sXfEHYs48Dp71lmAg2DCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"942bf592cd0df28cc7b5509ba8db5115bbecc5fe9fd450fd1c90e8ccf75ade5d","last_reissued_at":"2026-05-18T00:42:07.362882Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:07.362882Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Epameinondas Antonakos, George Trigeorgis, Iasonas Kokkinos, Patrick Snape, R{\\i}za Alp G\\\"uler, Stefanos Zafeiriou","submitted_at":"2016-12-04T23:08:06Z","abstract_excerpt":"In this paper we propose to learn a mapping from image pixels into a dense template grid through a fully convolutional network. We formulate this task as a regression problem and train our network by leveraging upon manually annotated facial landmarks \"in-the-wild\". We use such landmarks to establish a dense correspondence field between a three-dimensional object template and the input image, which then serves as the ground-truth for training our regression system. We show that we can combine ideas from semantic segmentation with regression networks, yielding a highly-accurate \"quantized regre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.01202","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":"1612.01202","created_at":"2026-05-18T00:42:07.362956+00:00"},{"alias_kind":"arxiv_version","alias_value":"1612.01202v2","created_at":"2026-05-18T00:42:07.362956+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.01202","created_at":"2026-05-18T00:42:07.362956+00:00"},{"alias_kind":"pith_short_12","alias_value":"SQV7LEWNBXZI","created_at":"2026-05-18T12:30:44.179134+00:00"},{"alias_kind":"pith_short_16","alias_value":"SQV7LEWNBXZIZR5V","created_at":"2026-05-18T12:30:44.179134+00:00"},{"alias_kind":"pith_short_8","alias_value":"SQV7LEWN","created_at":"2026-05-18T12:30:44.179134+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/SQV7LEWNBXZIZR5VKCN2RW2RCW","json":"https://pith.science/pith/SQV7LEWNBXZIZR5VKCN2RW2RCW.json","graph_json":"https://pith.science/api/pith-number/SQV7LEWNBXZIZR5VKCN2RW2RCW/graph.json","events_json":"https://pith.science/api/pith-number/SQV7LEWNBXZIZR5VKCN2RW2RCW/events.json","paper":"https://pith.science/paper/SQV7LEWN"},"agent_actions":{"view_html":"https://pith.science/pith/SQV7LEWNBXZIZR5VKCN2RW2RCW","download_json":"https://pith.science/pith/SQV7LEWNBXZIZR5VKCN2RW2RCW.json","view_paper":"https://pith.science/paper/SQV7LEWN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1612.01202&json=true","fetch_graph":"https://pith.science/api/pith-number/SQV7LEWNBXZIZR5VKCN2RW2RCW/graph.json","fetch_events":"https://pith.science/api/pith-number/SQV7LEWNBXZIZR5VKCN2RW2RCW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SQV7LEWNBXZIZR5VKCN2RW2RCW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SQV7LEWNBXZIZR5VKCN2RW2RCW/action/storage_attestation","attest_author":"https://pith.science/pith/SQV7LEWNBXZIZR5VKCN2RW2RCW/action/author_attestation","sign_citation":"https://pith.science/pith/SQV7LEWNBXZIZR5VKCN2RW2RCW/action/citation_signature","submit_replication":"https://pith.science/pith/SQV7LEWNBXZIZR5VKCN2RW2RCW/action/replication_record"}},"created_at":"2026-05-18T00:42:07.362956+00:00","updated_at":"2026-05-18T00:42:07.362956+00:00"}