{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:TFKSEAO5V3IRXGHK2NATZ6GQUY","short_pith_number":"pith:TFKSEAO5","schema_version":"1.0","canonical_sha256":"99552201ddaed11b98ead3413cf8d0a60921fb1acdd1c3b0e85154768a185e47","source":{"kind":"arxiv","id":"1901.01880","version":2},"attestation_state":"computed","paper":{"title":"Monocular Neural Image Based Rendering with Continuous View Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jie Song, Otmar Hilliges, Xu Chen","submitted_at":"2019-01-07T15:24:25Z","abstract_excerpt":"We present an approach that learns to synthesize high-quality, novel views of 3D objects or scenes, while providing fine-grained and precise control over the 6-DOF viewpoint. The approach is self-supervised and only requires 2D images and associated view transforms for training. Our main contribution is a network architecture that leverages a transforming auto-encoder in combination with a depth-guided warping procedure to predict geometrically accurate unseen views. Leveraging geometric constraints renders direct supervision via depth or flow maps unnecessary. If large parts of the object are"},"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":"1901.01880","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-07T15:24:25Z","cross_cats_sorted":[],"title_canon_sha256":"a96f21849dbb6a1e5fdcfa61dbc21c4e484c8108d4d53a22339b4c1888a66641","abstract_canon_sha256":"06c53edf79237892ac473409f78d5c356571d98d00433794b4b1178b9e61a61b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:02:56.219766Z","signature_b64":"39vwmAboLBgRx+a593JczIM48mOJ7K59S1xMp+bWG5KenkC48iSpSIOJ87l01vHH38dsS6Rz/Tst7sNj5sVSAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"99552201ddaed11b98ead3413cf8d0a60921fb1acdd1c3b0e85154768a185e47","last_reissued_at":"2026-07-05T00:02:56.219405Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:02:56.219405Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Monocular Neural Image Based Rendering with Continuous View Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jie Song, Otmar Hilliges, Xu Chen","submitted_at":"2019-01-07T15:24:25Z","abstract_excerpt":"We present an approach that learns to synthesize high-quality, novel views of 3D objects or scenes, while providing fine-grained and precise control over the 6-DOF viewpoint. The approach is self-supervised and only requires 2D images and associated view transforms for training. Our main contribution is a network architecture that leverages a transforming auto-encoder in combination with a depth-guided warping procedure to predict geometrically accurate unseen views. Leveraging geometric constraints renders direct supervision via depth or flow maps unnecessary. If large parts of the object are"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.01880","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1901.01880/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"1901.01880","created_at":"2026-07-05T00:02:56.219467+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.01880v2","created_at":"2026-07-05T00:02:56.219467+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.01880","created_at":"2026-07-05T00:02:56.219467+00:00"},{"alias_kind":"pith_short_12","alias_value":"TFKSEAO5V3IR","created_at":"2026-07-05T00:02:56.219467+00:00"},{"alias_kind":"pith_short_16","alias_value":"TFKSEAO5V3IRXGHK","created_at":"2026-07-05T00:02:56.219467+00:00"},{"alias_kind":"pith_short_8","alias_value":"TFKSEAO5","created_at":"2026-07-05T00:02:56.219467+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/TFKSEAO5V3IRXGHK2NATZ6GQUY","json":"https://pith.science/pith/TFKSEAO5V3IRXGHK2NATZ6GQUY.json","graph_json":"https://pith.science/api/pith-number/TFKSEAO5V3IRXGHK2NATZ6GQUY/graph.json","events_json":"https://pith.science/api/pith-number/TFKSEAO5V3IRXGHK2NATZ6GQUY/events.json","paper":"https://pith.science/paper/TFKSEAO5"},"agent_actions":{"view_html":"https://pith.science/pith/TFKSEAO5V3IRXGHK2NATZ6GQUY","download_json":"https://pith.science/pith/TFKSEAO5V3IRXGHK2NATZ6GQUY.json","view_paper":"https://pith.science/paper/TFKSEAO5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.01880&json=true","fetch_graph":"https://pith.science/api/pith-number/TFKSEAO5V3IRXGHK2NATZ6GQUY/graph.json","fetch_events":"https://pith.science/api/pith-number/TFKSEAO5V3IRXGHK2NATZ6GQUY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TFKSEAO5V3IRXGHK2NATZ6GQUY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TFKSEAO5V3IRXGHK2NATZ6GQUY/action/storage_attestation","attest_author":"https://pith.science/pith/TFKSEAO5V3IRXGHK2NATZ6GQUY/action/author_attestation","sign_citation":"https://pith.science/pith/TFKSEAO5V3IRXGHK2NATZ6GQUY/action/citation_signature","submit_replication":"https://pith.science/pith/TFKSEAO5V3IRXGHK2NATZ6GQUY/action/replication_record"}},"created_at":"2026-07-05T00:02:56.219467+00:00","updated_at":"2026-07-05T00:02:56.219467+00:00"}