{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:CZR2YBNMIFAM7TLVQCC3EUMLAO","short_pith_number":"pith:CZR2YBNM","canonical_record":{"source":{"id":"1906.00925","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-03T16:51:35Z","cross_cats_sorted":[],"title_canon_sha256":"640fe7ccb302100fbadb4efddb0aa52d603a1e27f18c6856b7e85564a5cb60b8","abstract_canon_sha256":"33820db49c67325ddbf0d1f6b55288f1fb8852063707911c49a28813ab774268"},"schema_version":"1.0"},"canonical_sha256":"1663ac05ac4140cfcd758085b2518b03be884f52ea4a7c001f63276441eb4aa0","source":{"kind":"arxiv","id":"1906.00925","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.00925","created_at":"2026-05-17T23:44:18Z"},{"alias_kind":"arxiv_version","alias_value":"1906.00925v2","created_at":"2026-05-17T23:44:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.00925","created_at":"2026-05-17T23:44:18Z"},{"alias_kind":"pith_short_12","alias_value":"CZR2YBNMIFAM","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"CZR2YBNMIFAM7TLV","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"CZR2YBNM","created_at":"2026-05-18T12:33:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:CZR2YBNMIFAM7TLVQCC3EUMLAO","target":"record","payload":{"canonical_record":{"source":{"id":"1906.00925","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-03T16:51:35Z","cross_cats_sorted":[],"title_canon_sha256":"640fe7ccb302100fbadb4efddb0aa52d603a1e27f18c6856b7e85564a5cb60b8","abstract_canon_sha256":"33820db49c67325ddbf0d1f6b55288f1fb8852063707911c49a28813ab774268"},"schema_version":"1.0"},"canonical_sha256":"1663ac05ac4140cfcd758085b2518b03be884f52ea4a7c001f63276441eb4aa0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:18.232366Z","signature_b64":"eMOYyobYAPe829qNh6rahVFI+1B8yjpBOSFoVnCuVKYz07N02kN4iZX46KDroPmb3jipZkAQ0WuCTtmP//vHDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1663ac05ac4140cfcd758085b2518b03be884f52ea4a7c001f63276441eb4aa0","last_reissued_at":"2026-05-17T23:44:18.231872Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:18.231872Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.00925","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:44:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7e06RK1g61r88JQtK9PHaTFGKkw2h8TYoPVpuCVpiFGGxNJoiAr0kWj5bOAA+USiix+jObi0tGzK32giRgT8CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T22:38:59.414120Z"},"content_sha256":"379737cc855ebc49c02d11688d22985f86d17bf510e2a74ef4ea41028183d541","schema_version":"1.0","event_id":"sha256:379737cc855ebc49c02d11688d22985f86d17bf510e2a74ef4ea41028183d541"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:CZR2YBNMIFAM7TLVQCC3EUMLAO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"3D Appearance Super-Resolution with Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Luc Van Gool, Marc Pollefeys, Radu Timofte, Vagia Tsiminaki, Yawei Li","submitted_at":"2019-06-03T16:51:35Z","abstract_excerpt":"We tackle the problem of retrieving high-resolution (HR) texture maps of objects that are captured from multiple view points. In the multi-view case, model-based super-resolution (SR) methods have been recently proved to recover high quality texture maps. On the other hand, the advent of deep learning-based methods has already a significant impact on the problem of video and image SR. Yet, a deep learning-based approach to super-resolve the appearance of 3D objects is still missing. The main limitation of exploiting the power of deep learning techniques in the multi-view case is the lack of da"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.00925","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:44:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gnLp+3BQGCyX2LciyEF7Pxjw8rJ/1PtYn7z/zT15MY5W2dHJF5zz4HNXpg7MEB88EAKRNiAktGCXqnnDCkNLAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T22:38:59.414925Z"},"content_sha256":"1e577ea7b240d891d975f4ff5efa0d0871032f5a4424dc7624ea0434e287fad8","schema_version":"1.0","event_id":"sha256:1e577ea7b240d891d975f4ff5efa0d0871032f5a4424dc7624ea0434e287fad8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CZR2YBNMIFAM7TLVQCC3EUMLAO/bundle.json","state_url":"https://pith.science/pith/CZR2YBNMIFAM7TLVQCC3EUMLAO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CZR2YBNMIFAM7TLVQCC3EUMLAO/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-11T22:38:59Z","links":{"resolver":"https://pith.science/pith/CZR2YBNMIFAM7TLVQCC3EUMLAO","bundle":"https://pith.science/pith/CZR2YBNMIFAM7TLVQCC3EUMLAO/bundle.json","state":"https://pith.science/pith/CZR2YBNMIFAM7TLVQCC3EUMLAO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CZR2YBNMIFAM7TLVQCC3EUMLAO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:CZR2YBNMIFAM7TLVQCC3EUMLAO","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"33820db49c67325ddbf0d1f6b55288f1fb8852063707911c49a28813ab774268","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-03T16:51:35Z","title_canon_sha256":"640fe7ccb302100fbadb4efddb0aa52d603a1e27f18c6856b7e85564a5cb60b8"},"schema_version":"1.0","source":{"id":"1906.00925","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.00925","created_at":"2026-05-17T23:44:18Z"},{"alias_kind":"arxiv_version","alias_value":"1906.00925v2","created_at":"2026-05-17T23:44:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.00925","created_at":"2026-05-17T23:44:18Z"},{"alias_kind":"pith_short_12","alias_value":"CZR2YBNMIFAM","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"CZR2YBNMIFAM7TLV","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"CZR2YBNM","created_at":"2026-05-18T12:33:15Z"}],"graph_snapshots":[{"event_id":"sha256:1e577ea7b240d891d975f4ff5efa0d0871032f5a4424dc7624ea0434e287fad8","target":"graph","created_at":"2026-05-17T23:44:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"We tackle the problem of retrieving high-resolution (HR) texture maps of objects that are captured from multiple view points. In the multi-view case, model-based super-resolution (SR) methods have been recently proved to recover high quality texture maps. On the other hand, the advent of deep learning-based methods has already a significant impact on the problem of video and image SR. Yet, a deep learning-based approach to super-resolve the appearance of 3D objects is still missing. The main limitation of exploiting the power of deep learning techniques in the multi-view case is the lack of da","authors_text":"Luc Van Gool, Marc Pollefeys, Radu Timofte, Vagia Tsiminaki, Yawei Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-03T16:51:35Z","title":"3D Appearance Super-Resolution with Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.00925","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:379737cc855ebc49c02d11688d22985f86d17bf510e2a74ef4ea41028183d541","target":"record","created_at":"2026-05-17T23:44:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"33820db49c67325ddbf0d1f6b55288f1fb8852063707911c49a28813ab774268","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-03T16:51:35Z","title_canon_sha256":"640fe7ccb302100fbadb4efddb0aa52d603a1e27f18c6856b7e85564a5cb60b8"},"schema_version":"1.0","source":{"id":"1906.00925","kind":"arxiv","version":2}},"canonical_sha256":"1663ac05ac4140cfcd758085b2518b03be884f52ea4a7c001f63276441eb4aa0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1663ac05ac4140cfcd758085b2518b03be884f52ea4a7c001f63276441eb4aa0","first_computed_at":"2026-05-17T23:44:18.231872Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:18.231872Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eMOYyobYAPe829qNh6rahVFI+1B8yjpBOSFoVnCuVKYz07N02kN4iZX46KDroPmb3jipZkAQ0WuCTtmP//vHDg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:18.232366Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.00925","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:379737cc855ebc49c02d11688d22985f86d17bf510e2a74ef4ea41028183d541","sha256:1e577ea7b240d891d975f4ff5efa0d0871032f5a4424dc7624ea0434e287fad8"],"state_sha256":"7fff058f4531c2e47046ed9cc5e3f81dba1f0b50bf6c6fadc03d70f62523a519"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IJe5G1wt/x6aqZvouKgVAFx5J98Ww6FCpj6aiCe7hbHXCcClw4dgUkC5GmlK1RmsKavJyRDDLXZfGcrzImceBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T22:38:59.418987Z","bundle_sha256":"8d644b8727b149d67850beb931f0cd01bd534f6129324e0384837c28790cabf7"}}