{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:TYSGT4SMRPZ47VIVEOFWSD6VHM","short_pith_number":"pith:TYSGT4SM","schema_version":"1.0","canonical_sha256":"9e2469f24c8bf3cfd515238b690fd53b1c22f7a3f6b331ee23260ee9b5178e77","source":{"kind":"arxiv","id":"2501.10928","version":2},"attestation_state":"computed","paper":{"title":"Generative Physical AI in Vision: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Ajmal Mian, Anh-Dung Dinh, Chang Xu, Daochang Liu, Eunbyung Park, Junyu Zhang, Mubarak Shah, Shichao Zhang","submitted_at":"2025-01-19T03:19:47Z","abstract_excerpt":"Generative Artificial Intelligence (AI) has rapidly advanced the field of computer vision by enabling machines to create and interpret visual data with unprecedented sophistication. This transformation builds upon a foundation of generative models to produce realistic images, videos, and 3D/4D content. Conventional generative models primarily focus on visual fidelity while often neglecting the physical plausibility of the generated content. This gap limits their effectiveness in applications that require adherence to real-world physical laws, such as robotics, autonomous systems, and scientifi"},"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":"2501.10928","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-19T03:19:47Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9cd06c5638893bad46776afb8d0b4bbada824859ce75c9832e35dffe4706a860","abstract_canon_sha256":"5960da5d9cd57ba97f2574ba0af0abdd2916b9a8d49196575411976776e5802f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:51:31.396908Z","signature_b64":"ZlMU97bopwARuPmIBDMx0hHYjgDZtZlZBHGEYxoQuhMSQcr45QGP8ILQxrUJerSpCRztgGDc1Ss9fwICCU6fBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9e2469f24c8bf3cfd515238b690fd53b1c22f7a3f6b331ee23260ee9b5178e77","last_reissued_at":"2026-07-05T10:51:31.396422Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:51:31.396422Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generative Physical AI in Vision: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Ajmal Mian, Anh-Dung Dinh, Chang Xu, Daochang Liu, Eunbyung Park, Junyu Zhang, Mubarak Shah, Shichao Zhang","submitted_at":"2025-01-19T03:19:47Z","abstract_excerpt":"Generative Artificial Intelligence (AI) has rapidly advanced the field of computer vision by enabling machines to create and interpret visual data with unprecedented sophistication. This transformation builds upon a foundation of generative models to produce realistic images, videos, and 3D/4D content. Conventional generative models primarily focus on visual fidelity while often neglecting the physical plausibility of the generated content. This gap limits their effectiveness in applications that require adherence to real-world physical laws, such as robotics, autonomous systems, and scientifi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.10928","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/2501.10928/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":"2501.10928","created_at":"2026-07-05T10:51:31.396482+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.10928v2","created_at":"2026-07-05T10:51:31.396482+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.10928","created_at":"2026-07-05T10:51:31.396482+00:00"},{"alias_kind":"pith_short_12","alias_value":"TYSGT4SMRPZ4","created_at":"2026-07-05T10:51:31.396482+00:00"},{"alias_kind":"pith_short_16","alias_value":"TYSGT4SMRPZ47VIV","created_at":"2026-07-05T10:51:31.396482+00:00"},{"alias_kind":"pith_short_8","alias_value":"TYSGT4SM","created_at":"2026-07-05T10:51:31.396482+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.26916","citing_title":"PhysRAG: Enhancing Physics-Awareness in Video Generation via Retrieval-Augmented Generation","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2501.09038","citing_title":"Do generative video models understand physical principles?","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2605.19242","citing_title":"PhyWorld: Physics-Faithful World Model for Video Generation","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2509.24702","citing_title":"Enhancing Physical Plausibility in Video Generation by Reasoning the Implausibility","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2510.04978","citing_title":"Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI","ref_index":53,"is_internal_anchor":false},{"citing_arxiv_id":"2512.05564","citing_title":"ProPhy: Progressive Physical Alignment for Dynamic World Simulation","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2509.20328","citing_title":"Video models are zero-shot learners and reasoners","ref_index":48,"is_internal_anchor":false},{"citing_arxiv_id":"2604.16592","citing_title":"Human Cognition in Machines: A Unified Perspective of World Models","ref_index":110,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TYSGT4SMRPZ47VIVEOFWSD6VHM","json":"https://pith.science/pith/TYSGT4SMRPZ47VIVEOFWSD6VHM.json","graph_json":"https://pith.science/api/pith-number/TYSGT4SMRPZ47VIVEOFWSD6VHM/graph.json","events_json":"https://pith.science/api/pith-number/TYSGT4SMRPZ47VIVEOFWSD6VHM/events.json","paper":"https://pith.science/paper/TYSGT4SM"},"agent_actions":{"view_html":"https://pith.science/pith/TYSGT4SMRPZ47VIVEOFWSD6VHM","download_json":"https://pith.science/pith/TYSGT4SMRPZ47VIVEOFWSD6VHM.json","view_paper":"https://pith.science/paper/TYSGT4SM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.10928&json=true","fetch_graph":"https://pith.science/api/pith-number/TYSGT4SMRPZ47VIVEOFWSD6VHM/graph.json","fetch_events":"https://pith.science/api/pith-number/TYSGT4SMRPZ47VIVEOFWSD6VHM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TYSGT4SMRPZ47VIVEOFWSD6VHM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TYSGT4SMRPZ47VIVEOFWSD6VHM/action/storage_attestation","attest_author":"https://pith.science/pith/TYSGT4SMRPZ47VIVEOFWSD6VHM/action/author_attestation","sign_citation":"https://pith.science/pith/TYSGT4SMRPZ47VIVEOFWSD6VHM/action/citation_signature","submit_replication":"https://pith.science/pith/TYSGT4SMRPZ47VIVEOFWSD6VHM/action/replication_record"}},"created_at":"2026-07-05T10:51:31.396482+00:00","updated_at":"2026-07-05T10:51:31.396482+00:00"}