{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CSF3PKJCTF2ZVWUQ6XYDGUEUTE","short_pith_number":"pith:CSF3PKJC","schema_version":"1.0","canonical_sha256":"148bb7a92299759ada90f5f03350949903d6d54ac20ebaa2c41e06e17717e0f1","source":{"kind":"arxiv","id":"2508.05236","version":1},"attestation_state":"computed","paper":{"title":"ArbiViewGen: Controllable Arbitrary Viewpoint Camera Data Generation for Autonomous Driving via Stable Diffusion Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jingfeng Chen, Lei He, Yatong Lan, Yiru Wang","submitted_at":"2025-08-07T10:24:47Z","abstract_excerpt":"Arbitrary viewpoint image generation holds significant potential for autonomous driving, yet remains a challenging task due to the lack of ground-truth data for extrapolated views, which hampers the training of high-fidelity generative models. In this work, we propose Arbiviewgen, a novel diffusion-based framework for the generation of controllable camera images from arbitrary points of view. To address the absence of ground-truth data in unseen views, we introduce two key components: Feature-Aware Adaptive View Stitching (FAVS) and Cross-View Consistency Self-Supervised Learning (CVC-SSL). FA"},"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":"2508.05236","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-08-07T10:24:47Z","cross_cats_sorted":[],"title_canon_sha256":"3e92363b2e0797642c5affc178713c7130cee6456eb879481b913c7b514d144a","abstract_canon_sha256":"cd9a648f14b475e132b86b754aaee52ae9110e52609e07405430a6f41ea1a4ba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:50:09.435203Z","signature_b64":"eBLRweGkqYPCA2yOU43Kw/BiVsT9Dqt0MmwKRx7/wlLRpuA5M5E5fBlsKdYDsfdXiV64l9AM1SgWidSoT/P9Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"148bb7a92299759ada90f5f03350949903d6d54ac20ebaa2c41e06e17717e0f1","last_reissued_at":"2026-07-05T11:50:09.434712Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:50:09.434712Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ArbiViewGen: Controllable Arbitrary Viewpoint Camera Data Generation for Autonomous Driving via Stable Diffusion Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jingfeng Chen, Lei He, Yatong Lan, Yiru Wang","submitted_at":"2025-08-07T10:24:47Z","abstract_excerpt":"Arbitrary viewpoint image generation holds significant potential for autonomous driving, yet remains a challenging task due to the lack of ground-truth data for extrapolated views, which hampers the training of high-fidelity generative models. In this work, we propose Arbiviewgen, a novel diffusion-based framework for the generation of controllable camera images from arbitrary points of view. To address the absence of ground-truth data in unseen views, we introduce two key components: Feature-Aware Adaptive View Stitching (FAVS) and Cross-View Consistency Self-Supervised Learning (CVC-SSL). FA"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.05236","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2508.05236/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":"2508.05236","created_at":"2026-07-05T11:50:09.434773+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.05236v1","created_at":"2026-07-05T11:50:09.434773+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.05236","created_at":"2026-07-05T11:50:09.434773+00:00"},{"alias_kind":"pith_short_12","alias_value":"CSF3PKJCTF2Z","created_at":"2026-07-05T11:50:09.434773+00:00"},{"alias_kind":"pith_short_16","alias_value":"CSF3PKJCTF2ZVWUQ","created_at":"2026-07-05T11:50:09.434773+00:00"},{"alias_kind":"pith_short_8","alias_value":"CSF3PKJC","created_at":"2026-07-05T11:50:09.434773+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2512.15422","citing_title":"A Survey on the Applications of Generative Artificial Intelligence in Automated Driving Systems Test Scenario Generation Methods","ref_index":86,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CSF3PKJCTF2ZVWUQ6XYDGUEUTE","json":"https://pith.science/pith/CSF3PKJCTF2ZVWUQ6XYDGUEUTE.json","graph_json":"https://pith.science/api/pith-number/CSF3PKJCTF2ZVWUQ6XYDGUEUTE/graph.json","events_json":"https://pith.science/api/pith-number/CSF3PKJCTF2ZVWUQ6XYDGUEUTE/events.json","paper":"https://pith.science/paper/CSF3PKJC"},"agent_actions":{"view_html":"https://pith.science/pith/CSF3PKJCTF2ZVWUQ6XYDGUEUTE","download_json":"https://pith.science/pith/CSF3PKJCTF2ZVWUQ6XYDGUEUTE.json","view_paper":"https://pith.science/paper/CSF3PKJC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.05236&json=true","fetch_graph":"https://pith.science/api/pith-number/CSF3PKJCTF2ZVWUQ6XYDGUEUTE/graph.json","fetch_events":"https://pith.science/api/pith-number/CSF3PKJCTF2ZVWUQ6XYDGUEUTE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CSF3PKJCTF2ZVWUQ6XYDGUEUTE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CSF3PKJCTF2ZVWUQ6XYDGUEUTE/action/storage_attestation","attest_author":"https://pith.science/pith/CSF3PKJCTF2ZVWUQ6XYDGUEUTE/action/author_attestation","sign_citation":"https://pith.science/pith/CSF3PKJCTF2ZVWUQ6XYDGUEUTE/action/citation_signature","submit_replication":"https://pith.science/pith/CSF3PKJCTF2ZVWUQ6XYDGUEUTE/action/replication_record"}},"created_at":"2026-07-05T11:50:09.434773+00:00","updated_at":"2026-07-05T11:50:09.434773+00:00"}