{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:EQU6HILVGKZHXECJWPEBPAFO26","short_pith_number":"pith:EQU6HILV","canonical_record":{"source":{"id":"1811.10519","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-26T17:21:30Z","cross_cats_sorted":[],"title_canon_sha256":"25748b426fb53da0ac83c862fe661cb2cbb60f3a96c04a7ee157c643b9428df6","abstract_canon_sha256":"0118435f6ef5e1cab530314d4665d89ff09715514faddb9fed64f72e4fb89a9f"},"schema_version":"1.0"},"canonical_sha256":"2429e3a17532b27b9049b3c81780aed7b89062e865fd61f18fa526174fc7285c","source":{"kind":"arxiv","id":"1811.10519","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.10519","created_at":"2026-05-17T23:59:47Z"},{"alias_kind":"arxiv_version","alias_value":"1811.10519v2","created_at":"2026-05-17T23:59:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.10519","created_at":"2026-05-17T23:59:47Z"},{"alias_kind":"pith_short_12","alias_value":"EQU6HILVGKZH","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"EQU6HILVGKZHXECJ","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"EQU6HILV","created_at":"2026-05-18T12:32:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:EQU6HILVGKZHXECJWPEBPAFO26","target":"record","payload":{"canonical_record":{"source":{"id":"1811.10519","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-26T17:21:30Z","cross_cats_sorted":[],"title_canon_sha256":"25748b426fb53da0ac83c862fe661cb2cbb60f3a96c04a7ee157c643b9428df6","abstract_canon_sha256":"0118435f6ef5e1cab530314d4665d89ff09715514faddb9fed64f72e4fb89a9f"},"schema_version":"1.0"},"canonical_sha256":"2429e3a17532b27b9049b3c81780aed7b89062e865fd61f18fa526174fc7285c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:47.983060Z","signature_b64":"0CIgAF44skehjmoqzEBtddL7yWf/tnY1j60votJpvI4jaR9ESv4WFJ0mvvB0ThjALLEmU8+LRP/pChDN/cOJDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2429e3a17532b27b9049b3c81780aed7b89062e865fd61f18fa526174fc7285c","last_reissued_at":"2026-05-17T23:59:47.982496Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:47.982496Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.10519","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":"2026-05-17T23:59:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5yauBFkvGCiW7+suKVYH2YcMZPQqiFpIsyYVhwCB8ch1miT+0q8x8DhW4oudZAidar0UWKstbz0Xq2HzmNe/Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T07:22:24.465589Z"},"content_sha256":"f5aee323f78c9c3a7cee357d7fcd18e54b594e9374b08bf26bd3e8b51fec4c63","schema_version":"1.0","event_id":"sha256:f5aee323f78c9c3a7cee357d7fcd18e54b594e9374b08bf26bd3e8b51fec4c63"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:EQU6HILVGKZHXECJWPEBPAFO26","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unsupervised 3D Shape Learning from Image Collections in the Wild","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Attila Szab\\'o, Paolo Favaro","submitted_at":"2018-11-26T17:21:30Z","abstract_excerpt":"We present a method to learn the 3D surface of objects directly from a collection of images. Previous work achieved this capability by exploiting additional manual annotation, such as object pose, 3D surface templates, temporal continuity of videos, manually selected landmarks, and foreground/background masks. In contrast, our method does not make use of any such annotation. Rather, it builds a generative model, a convolutional neural network, which, given a noise vector sample, outputs the 3D surface and texture of an object and a background image. These 3 components combined with an addition"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.10519","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":"2026-05-17T23:59:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B9z7Xgwj7vBMo438k0loZEHQHHIOuWsl/qz9SnpaK9QDue3t5UpMxxvUCC05vV/u0lSYZNuuJib03DiES6rzDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T07:22:24.465878Z"},"content_sha256":"69feab8a41b150327b9217ef470b63482c09d310ecccfd31c119ebc4286419a9","schema_version":"1.0","event_id":"sha256:69feab8a41b150327b9217ef470b63482c09d310ecccfd31c119ebc4286419a9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EQU6HILVGKZHXECJWPEBPAFO26/bundle.json","state_url":"https://pith.science/pith/EQU6HILVGKZHXECJWPEBPAFO26/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EQU6HILVGKZHXECJWPEBPAFO26/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":"2026-08-19T07:22:24Z","links":{"resolver":"https://pith.science/pith/EQU6HILVGKZHXECJWPEBPAFO26","bundle":"https://pith.science/pith/EQU6HILVGKZHXECJWPEBPAFO26/bundle.json","state":"https://pith.science/pith/EQU6HILVGKZHXECJWPEBPAFO26/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EQU6HILVGKZHXECJWPEBPAFO26/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:EQU6HILVGKZHXECJWPEBPAFO26","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":"0118435f6ef5e1cab530314d4665d89ff09715514faddb9fed64f72e4fb89a9f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-26T17:21:30Z","title_canon_sha256":"25748b426fb53da0ac83c862fe661cb2cbb60f3a96c04a7ee157c643b9428df6"},"schema_version":"1.0","source":{"id":"1811.10519","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.10519","created_at":"2026-05-17T23:59:47Z"},{"alias_kind":"arxiv_version","alias_value":"1811.10519v2","created_at":"2026-05-17T23:59:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.10519","created_at":"2026-05-17T23:59:47Z"},{"alias_kind":"pith_short_12","alias_value":"EQU6HILVGKZH","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"EQU6HILVGKZHXECJ","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"EQU6HILV","created_at":"2026-05-18T12:32:22Z"}],"graph_snapshots":[{"event_id":"sha256:69feab8a41b150327b9217ef470b63482c09d310ecccfd31c119ebc4286419a9","target":"graph","created_at":"2026-05-17T23:59:47Z","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 present a method to learn the 3D surface of objects directly from a collection of images. Previous work achieved this capability by exploiting additional manual annotation, such as object pose, 3D surface templates, temporal continuity of videos, manually selected landmarks, and foreground/background masks. In contrast, our method does not make use of any such annotation. Rather, it builds a generative model, a convolutional neural network, which, given a noise vector sample, outputs the 3D surface and texture of an object and a background image. These 3 components combined with an addition","authors_text":"Attila Szab\\'o, Paolo Favaro","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-26T17:21:30Z","title":"Unsupervised 3D Shape Learning from Image Collections in the Wild"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.10519","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:f5aee323f78c9c3a7cee357d7fcd18e54b594e9374b08bf26bd3e8b51fec4c63","target":"record","created_at":"2026-05-17T23:59:47Z","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":"0118435f6ef5e1cab530314d4665d89ff09715514faddb9fed64f72e4fb89a9f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-26T17:21:30Z","title_canon_sha256":"25748b426fb53da0ac83c862fe661cb2cbb60f3a96c04a7ee157c643b9428df6"},"schema_version":"1.0","source":{"id":"1811.10519","kind":"arxiv","version":2}},"canonical_sha256":"2429e3a17532b27b9049b3c81780aed7b89062e865fd61f18fa526174fc7285c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2429e3a17532b27b9049b3c81780aed7b89062e865fd61f18fa526174fc7285c","first_computed_at":"2026-05-17T23:59:47.982496Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:59:47.982496Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0CIgAF44skehjmoqzEBtddL7yWf/tnY1j60votJpvI4jaR9ESv4WFJ0mvvB0ThjALLEmU8+LRP/pChDN/cOJDQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:59:47.983060Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.10519","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f5aee323f78c9c3a7cee357d7fcd18e54b594e9374b08bf26bd3e8b51fec4c63","sha256:69feab8a41b150327b9217ef470b63482c09d310ecccfd31c119ebc4286419a9"],"state_sha256":"73c4065cb1c3cc996da6548cb1ec816d42ba71d20a31729c8b11f2d53a7c3eeb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GCx/wT1bCAIAZkJHxSO0EkzDE17yrM6IE+C2AMBYCbtUbdz1OcC8QsYaeLYU1JnSxxKFT0Ewc/Sonwb2teGrBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T07:22:24.469093Z","bundle_sha256":"7bef1dd1d8be043765c2e82be969db95b9a5575dccc19ee0c499b23702da6812"}}