{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:UAYSO5AHGBTYCRVW4N2ZUGRJRY","short_pith_number":"pith:UAYSO5AH","schema_version":"1.0","canonical_sha256":"a03127740730678146b6e3759a1a298e1dbd8d105738dc06691479fc435434c8","source":{"kind":"arxiv","id":"1703.02921","version":1},"attestation_state":"computed","paper":{"title":"Transformation-Grounded Image Generation Network for Novel 3D View Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexander C. Berg, Duygu Ceylan, Ersin Yumer, Eunbyung Park, Jimei Yang","submitted_at":"2017-03-08T17:16:15Z","abstract_excerpt":"We present a transformation-grounded image generation network for novel 3D view synthesis from a single image. Instead of taking a 'blank slate' approach, we first explicitly infer the parts of the geometry visible both in the input and novel views and then re-cast the remaining synthesis problem as image completion. Specifically, we both predict a flow to move the pixels from the input to the novel view along with a novel visibility map that helps deal with occulsion/disocculsion. Next, conditioned on those intermediate results, we hallucinate (infer) parts of the object invisible in the inpu"},"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":"1703.02921","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-08T17:16:15Z","cross_cats_sorted":[],"title_canon_sha256":"f11785ad11211cfadf3922ce465d7d20b74ebccfe44310aecadf033c4ccd3420","abstract_canon_sha256":"8bc01b3b2708e44b33a4deb3be9651ebef6436ade2acf398bfa8a62008b9d646"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:49:05.313536Z","signature_b64":"HxArTX8wc28UH6I50UQhKlsEpmuxElVaDUOA9UjeSTLoGnf/G6+Pnk6SVloxKRw6IV/d6WhYWD2wUjNTxeI8Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a03127740730678146b6e3759a1a298e1dbd8d105738dc06691479fc435434c8","last_reissued_at":"2026-05-18T00:49:05.313047Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:49:05.313047Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Transformation-Grounded Image Generation Network for Novel 3D View Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexander C. Berg, Duygu Ceylan, Ersin Yumer, Eunbyung Park, Jimei Yang","submitted_at":"2017-03-08T17:16:15Z","abstract_excerpt":"We present a transformation-grounded image generation network for novel 3D view synthesis from a single image. Instead of taking a 'blank slate' approach, we first explicitly infer the parts of the geometry visible both in the input and novel views and then re-cast the remaining synthesis problem as image completion. Specifically, we both predict a flow to move the pixels from the input to the novel view along with a novel visibility map that helps deal with occulsion/disocculsion. Next, conditioned on those intermediate results, we hallucinate (infer) parts of the object invisible in the inpu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.02921","kind":"arxiv","version":1},"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":"1703.02921","created_at":"2026-05-18T00:49:05.313130+00:00"},{"alias_kind":"arxiv_version","alias_value":"1703.02921v1","created_at":"2026-05-18T00:49:05.313130+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.02921","created_at":"2026-05-18T00:49:05.313130+00:00"},{"alias_kind":"pith_short_12","alias_value":"UAYSO5AHGBTY","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_16","alias_value":"UAYSO5AHGBTYCRVW","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_8","alias_value":"UAYSO5AH","created_at":"2026-05-18T12:31:46.661854+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.05710","citing_title":"SSDD-GAN: Single-Step Denoising Diffusion GAN for Cochlear Implant Surgical Scene Completion","ref_index":9,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UAYSO5AHGBTYCRVW4N2ZUGRJRY","json":"https://pith.science/pith/UAYSO5AHGBTYCRVW4N2ZUGRJRY.json","graph_json":"https://pith.science/api/pith-number/UAYSO5AHGBTYCRVW4N2ZUGRJRY/graph.json","events_json":"https://pith.science/api/pith-number/UAYSO5AHGBTYCRVW4N2ZUGRJRY/events.json","paper":"https://pith.science/paper/UAYSO5AH"},"agent_actions":{"view_html":"https://pith.science/pith/UAYSO5AHGBTYCRVW4N2ZUGRJRY","download_json":"https://pith.science/pith/UAYSO5AHGBTYCRVW4N2ZUGRJRY.json","view_paper":"https://pith.science/paper/UAYSO5AH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1703.02921&json=true","fetch_graph":"https://pith.science/api/pith-number/UAYSO5AHGBTYCRVW4N2ZUGRJRY/graph.json","fetch_events":"https://pith.science/api/pith-number/UAYSO5AHGBTYCRVW4N2ZUGRJRY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UAYSO5AHGBTYCRVW4N2ZUGRJRY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UAYSO5AHGBTYCRVW4N2ZUGRJRY/action/storage_attestation","attest_author":"https://pith.science/pith/UAYSO5AHGBTYCRVW4N2ZUGRJRY/action/author_attestation","sign_citation":"https://pith.science/pith/UAYSO5AHGBTYCRVW4N2ZUGRJRY/action/citation_signature","submit_replication":"https://pith.science/pith/UAYSO5AHGBTYCRVW4N2ZUGRJRY/action/replication_record"}},"created_at":"2026-05-18T00:49:05.313130+00:00","updated_at":"2026-05-18T00:49:05.313130+00:00"}