{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:33GI6T3YSUF7WOZK5KWWFPHHNN","short_pith_number":"pith:33GI6T3Y","canonical_record":{"source":{"id":"1809.04696","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-12T22:38:22Z","cross_cats_sorted":[],"title_canon_sha256":"e9e6242b0edda7d872335d526befd0b8595a1deb5ebf11236f397c5b3dfda1a8","abstract_canon_sha256":"2693d74fb0b3882006ad8c4cfcb4f3c10798d8be9b3275f3f9d22503a2ce2143"},"schema_version":"1.0"},"canonical_sha256":"decc8f4f78950bfb3b2aeaad62bce76b7e05e74620c86ac4cfc2349c78573747","source":{"kind":"arxiv","id":"1809.04696","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.04696","created_at":"2026-05-17T23:59:22Z"},{"alias_kind":"arxiv_version","alias_value":"1809.04696v2","created_at":"2026-05-17T23:59:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.04696","created_at":"2026-05-17T23:59:22Z"},{"alias_kind":"pith_short_12","alias_value":"33GI6T3YSUF7","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"33GI6T3YSUF7WOZK","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"33GI6T3Y","created_at":"2026-05-18T12:32:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:33GI6T3YSUF7WOZK5KWWFPHHNN","target":"record","payload":{"canonical_record":{"source":{"id":"1809.04696","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-12T22:38:22Z","cross_cats_sorted":[],"title_canon_sha256":"e9e6242b0edda7d872335d526befd0b8595a1deb5ebf11236f397c5b3dfda1a8","abstract_canon_sha256":"2693d74fb0b3882006ad8c4cfcb4f3c10798d8be9b3275f3f9d22503a2ce2143"},"schema_version":"1.0"},"canonical_sha256":"decc8f4f78950bfb3b2aeaad62bce76b7e05e74620c86ac4cfc2349c78573747","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:22.293608Z","signature_b64":"gAemEGwfCxgtFZPmA0HMJQslS8grJwMAr4POf0h/qWRFayHjAX/wZuctv+CjFxvH+Q2Dbi+cXoso+oL19rk3Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"decc8f4f78950bfb3b2aeaad62bce76b7e05e74620c86ac4cfc2349c78573747","last_reissued_at":"2026-05-17T23:59:22.293260Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:22.293260Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.04696","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":"2026-05-17T23:59:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZEAkijpsGjjp2NHuff2jg/VI2pIxc3LSEhif3l3svgLVRNVL50tRecGCtw4oemctudwsCkk1XbHKZxGbf5MDCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T22:52:07.446901Z"},"content_sha256":"1b9b06e586bb5936fb479f05a7ebb3b8df8502877284282690de604bfbc9a46f","schema_version":"1.0","event_id":"sha256:1b9b06e586bb5936fb479f05a7ebb3b8df8502877284282690de604bfbc9a46f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:33GI6T3YSUF7WOZK5KWWFPHHNN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Geometric Image Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andreas Geiger, Carsten Rother, Hassan Abu Alhaija, Siva Karthik Mustikovela","submitted_at":"2018-09-12T22:38:22Z","abstract_excerpt":"The task of generating natural images from 3D scenes has been a long standing goal in computer graphics. On the other hand, recent developments in deep neural networks allow for trainable models that can produce natural-looking images with little or no knowledge about the scene structure. While the generated images often consist of realistic looking local patterns, the overall structure of the generated images is often inconsistent. In this work we propose a trainable, geometry-aware image generation method that leverages various types of scene information, including geometry and segmentation,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.04696","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":"2026-05-17T23:59:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MhG8Ehv8eIKd2qgG5e4D9bF9SnMft03JeMZOoolntIE3dGvxVDCHLt719C8RdaaLb+gTswySLNTNjxTA/KLqDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T22:52:07.447577Z"},"content_sha256":"9dfba9b1435d144030cc5601a321662f0463748be1eb17d45523dbb31d42174a","schema_version":"1.0","event_id":"sha256:9dfba9b1435d144030cc5601a321662f0463748be1eb17d45523dbb31d42174a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/33GI6T3YSUF7WOZK5KWWFPHHNN/bundle.json","state_url":"https://pith.science/pith/33GI6T3YSUF7WOZK5KWWFPHHNN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/33GI6T3YSUF7WOZK5KWWFPHHNN/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":"2026-05-26T22:52:07Z","links":{"resolver":"https://pith.science/pith/33GI6T3YSUF7WOZK5KWWFPHHNN","bundle":"https://pith.science/pith/33GI6T3YSUF7WOZK5KWWFPHHNN/bundle.json","state":"https://pith.science/pith/33GI6T3YSUF7WOZK5KWWFPHHNN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/33GI6T3YSUF7WOZK5KWWFPHHNN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:33GI6T3YSUF7WOZK5KWWFPHHNN","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":"2693d74fb0b3882006ad8c4cfcb4f3c10798d8be9b3275f3f9d22503a2ce2143","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-12T22:38:22Z","title_canon_sha256":"e9e6242b0edda7d872335d526befd0b8595a1deb5ebf11236f397c5b3dfda1a8"},"schema_version":"1.0","source":{"id":"1809.04696","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.04696","created_at":"2026-05-17T23:59:22Z"},{"alias_kind":"arxiv_version","alias_value":"1809.04696v2","created_at":"2026-05-17T23:59:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.04696","created_at":"2026-05-17T23:59:22Z"},{"alias_kind":"pith_short_12","alias_value":"33GI6T3YSUF7","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"33GI6T3YSUF7WOZK","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"33GI6T3Y","created_at":"2026-05-18T12:32:02Z"}],"graph_snapshots":[{"event_id":"sha256:9dfba9b1435d144030cc5601a321662f0463748be1eb17d45523dbb31d42174a","target":"graph","created_at":"2026-05-17T23:59:22Z","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":"The task of generating natural images from 3D scenes has been a long standing goal in computer graphics. On the other hand, recent developments in deep neural networks allow for trainable models that can produce natural-looking images with little or no knowledge about the scene structure. While the generated images often consist of realistic looking local patterns, the overall structure of the generated images is often inconsistent. In this work we propose a trainable, geometry-aware image generation method that leverages various types of scene information, including geometry and segmentation,","authors_text":"Andreas Geiger, Carsten Rother, Hassan Abu Alhaija, Siva Karthik Mustikovela","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-12T22:38:22Z","title":"Geometric Image Synthesis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.04696","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":"sha256:1b9b06e586bb5936fb479f05a7ebb3b8df8502877284282690de604bfbc9a46f","target":"record","created_at":"2026-05-17T23:59:22Z","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":"2693d74fb0b3882006ad8c4cfcb4f3c10798d8be9b3275f3f9d22503a2ce2143","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-12T22:38:22Z","title_canon_sha256":"e9e6242b0edda7d872335d526befd0b8595a1deb5ebf11236f397c5b3dfda1a8"},"schema_version":"1.0","source":{"id":"1809.04696","kind":"arxiv","version":2}},"canonical_sha256":"decc8f4f78950bfb3b2aeaad62bce76b7e05e74620c86ac4cfc2349c78573747","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"decc8f4f78950bfb3b2aeaad62bce76b7e05e74620c86ac4cfc2349c78573747","first_computed_at":"2026-05-17T23:59:22.293260Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:59:22.293260Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gAemEGwfCxgtFZPmA0HMJQslS8grJwMAr4POf0h/qWRFayHjAX/wZuctv+CjFxvH+Q2Dbi+cXoso+oL19rk3Cw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:59:22.293608Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.04696","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1b9b06e586bb5936fb479f05a7ebb3b8df8502877284282690de604bfbc9a46f","sha256:9dfba9b1435d144030cc5601a321662f0463748be1eb17d45523dbb31d42174a"],"state_sha256":"d41666c906a3cb2633e6a3bfe7acc93c0eef8b2e7403d4d5bfbbc5c502acd76d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eQ2ThQancvLsHGKU2bsgLBRu/oCsJ5S38KTYTsHHO668dWKgopG1T7pn/DeRfHYk49w97/oVTbE3fp+YFwPNDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T22:52:07.450799Z","bundle_sha256":"223e4b6416fa0822aa11593a092c8d75fbb6eb55a7f0e8edc99f3d45a12e3861"}}