{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:PENUQCDHF5FB3ECYE5ZQKOVAKP","short_pith_number":"pith:PENUQCDH","schema_version":"1.0","canonical_sha256":"791b4808672f4a1d90582773053aa053f695823ff461ca2af5ab35598c42c9ef","source":{"kind":"arxiv","id":"2603.05425","version":2},"attestation_state":"computed","paper":{"title":"RelaxFlow: Text-Driven Amodal 3D Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Angela Yao, Guoji Fu, Jiayin Zhu, Qiyuan He, Xiaolu Liu, Yicong Li","submitted_at":"2026-03-05T17:45:47Z","abstract_excerpt":"Image-to-3D generation faces inherent semantic ambiguity under occlusion, where partial observation alone is often insufficient to determine object category. In this work, we formalize text-driven amodal 3D generation, where text prompts steer the completion of unseen regions while strictly preserving input observation. Crucially, we identify that these objectives demand distinct control granularities: rigid control for the observation versus relaxed structural control for the prompt. To this end, we propose RelaxFlow, a training-free dual-branch framework that decouples control granularity vi"},"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":"2603.05425","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-03-05T17:45:47Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"618f40e1eba2d136533dce29db874da665155ad5d29426196d0288d97211ddc2","abstract_canon_sha256":"1e0479e2d6691587ccea48393e8fdc287705c25d6e9e1c2a009d12e95d503ad0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T02:04:46.770376Z","signature_b64":"b/7EqYKdO1IFCz/MOVGO05Ndn4FY3zuV7WthQ1/5Kic3ykd3ocuK5RL4JpcOqVldiqdCXsLmHtTNpJDjh3isCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"791b4808672f4a1d90582773053aa053f695823ff461ca2af5ab35598c42c9ef","last_reissued_at":"2026-05-28T02:04:46.769854Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T02:04:46.769854Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RelaxFlow: Text-Driven Amodal 3D Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Angela Yao, Guoji Fu, Jiayin Zhu, Qiyuan He, Xiaolu Liu, Yicong Li","submitted_at":"2026-03-05T17:45:47Z","abstract_excerpt":"Image-to-3D generation faces inherent semantic ambiguity under occlusion, where partial observation alone is often insufficient to determine object category. In this work, we formalize text-driven amodal 3D generation, where text prompts steer the completion of unseen regions while strictly preserving input observation. Crucially, we identify that these objectives demand distinct control granularities: rigid control for the observation versus relaxed structural control for the prompt. To this end, we propose RelaxFlow, a training-free dual-branch framework that decouples control granularity vi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.05425","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/2603.05425/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":"2603.05425","created_at":"2026-05-28T02:04:46.769918+00:00"},{"alias_kind":"arxiv_version","alias_value":"2603.05425v2","created_at":"2026-05-28T02:04:46.769918+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.05425","created_at":"2026-05-28T02:04:46.769918+00:00"},{"alias_kind":"pith_short_12","alias_value":"PENUQCDHF5FB","created_at":"2026-05-28T02:04:46.769918+00:00"},{"alias_kind":"pith_short_16","alias_value":"PENUQCDHF5FB3ECY","created_at":"2026-05-28T02:04:46.769918+00:00"},{"alias_kind":"pith_short_8","alias_value":"PENUQCDH","created_at":"2026-05-28T02:04:46.769918+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/PENUQCDHF5FB3ECYE5ZQKOVAKP","json":"https://pith.science/pith/PENUQCDHF5FB3ECYE5ZQKOVAKP.json","graph_json":"https://pith.science/api/pith-number/PENUQCDHF5FB3ECYE5ZQKOVAKP/graph.json","events_json":"https://pith.science/api/pith-number/PENUQCDHF5FB3ECYE5ZQKOVAKP/events.json","paper":"https://pith.science/paper/PENUQCDH"},"agent_actions":{"view_html":"https://pith.science/pith/PENUQCDHF5FB3ECYE5ZQKOVAKP","download_json":"https://pith.science/pith/PENUQCDHF5FB3ECYE5ZQKOVAKP.json","view_paper":"https://pith.science/paper/PENUQCDH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2603.05425&json=true","fetch_graph":"https://pith.science/api/pith-number/PENUQCDHF5FB3ECYE5ZQKOVAKP/graph.json","fetch_events":"https://pith.science/api/pith-number/PENUQCDHF5FB3ECYE5ZQKOVAKP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PENUQCDHF5FB3ECYE5ZQKOVAKP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PENUQCDHF5FB3ECYE5ZQKOVAKP/action/storage_attestation","attest_author":"https://pith.science/pith/PENUQCDHF5FB3ECYE5ZQKOVAKP/action/author_attestation","sign_citation":"https://pith.science/pith/PENUQCDHF5FB3ECYE5ZQKOVAKP/action/citation_signature","submit_replication":"https://pith.science/pith/PENUQCDHF5FB3ECYE5ZQKOVAKP/action/replication_record"}},"created_at":"2026-05-28T02:04:46.769918+00:00","updated_at":"2026-05-28T02:04:46.769918+00:00"}