{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SAARRDIDEOOXYZ6JBJEGSSKQAG","short_pith_number":"pith:SAARRDID","schema_version":"1.0","canonical_sha256":"9001188d03239d7c67c90a48694950018cf9fdc5f3b0557cdd4dc78f16f6673d","source":{"kind":"arxiv","id":"2409.06189","version":2},"attestation_state":"computed","paper":{"title":"MyGo: Consistent and Controllable Multi-View Driving Video Generation with Camera Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chenjing Ding, Wei Wu, Xi Guo, Yining Yao","submitted_at":"2024-09-10T03:39:08Z","abstract_excerpt":"High-quality driving video generation is crucial for providing training data for autonomous driving models. However, current generative models rarely focus on enhancing camera motion control under multi-view tasks, which is essential for driving video generation. Therefore, we propose MyGo, an end-to-end framework for video generation, introducing motion of onboard cameras as conditions to make progress in camera controllability and multi-view consistency. MyGo employs additional plug-in modules to inject camera parameters into the pre-trained video diffusion model, which retains the extensive"},"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":"2409.06189","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-10T03:39:08Z","cross_cats_sorted":[],"title_canon_sha256":"acb91f4a4cba04029c9ef08c529b0aefd84b4ffb7c1a0e7fe06f2a6ff9991d9b","abstract_canon_sha256":"55427fec0468158c1c1894510c9e6fc9a192c1bda50242326fa72391951f939b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:05:39.451229Z","signature_b64":"BTlDeRn1AI6oGSnwpnaND+hDtXCkoXMPALnNBDN1G3f5MufQwOGI3r9PeWErcpzVOM2AmwC9BXHZ7aGwrkPVDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9001188d03239d7c67c90a48694950018cf9fdc5f3b0557cdd4dc78f16f6673d","last_reissued_at":"2026-07-05T09:05:39.450795Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:05:39.450795Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MyGo: Consistent and Controllable Multi-View Driving Video Generation with Camera Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chenjing Ding, Wei Wu, Xi Guo, Yining Yao","submitted_at":"2024-09-10T03:39:08Z","abstract_excerpt":"High-quality driving video generation is crucial for providing training data for autonomous driving models. However, current generative models rarely focus on enhancing camera motion control under multi-view tasks, which is essential for driving video generation. Therefore, we propose MyGo, an end-to-end framework for video generation, introducing motion of onboard cameras as conditions to make progress in camera controllability and multi-view consistency. MyGo employs additional plug-in modules to inject camera parameters into the pre-trained video diffusion model, which retains the extensive"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.06189","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/2409.06189/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":"2409.06189","created_at":"2026-07-05T09:05:39.450854+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.06189v2","created_at":"2026-07-05T09:05:39.450854+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.06189","created_at":"2026-07-05T09:05:39.450854+00:00"},{"alias_kind":"pith_short_12","alias_value":"SAARRDIDEOOX","created_at":"2026-07-05T09:05:39.450854+00:00"},{"alias_kind":"pith_short_16","alias_value":"SAARRDIDEOOXYZ6J","created_at":"2026-07-05T09:05:39.450854+00:00"},{"alias_kind":"pith_short_8","alias_value":"SAARRDID","created_at":"2026-07-05T09:05:39.450854+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.17536","citing_title":"OmniDrive: An LLM-Choreographed Multi-Agent World Model with Unified Latent Co-Compression for Multi-View Driving Video Generation","ref_index":62,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SAARRDIDEOOXYZ6JBJEGSSKQAG","json":"https://pith.science/pith/SAARRDIDEOOXYZ6JBJEGSSKQAG.json","graph_json":"https://pith.science/api/pith-number/SAARRDIDEOOXYZ6JBJEGSSKQAG/graph.json","events_json":"https://pith.science/api/pith-number/SAARRDIDEOOXYZ6JBJEGSSKQAG/events.json","paper":"https://pith.science/paper/SAARRDID"},"agent_actions":{"view_html":"https://pith.science/pith/SAARRDIDEOOXYZ6JBJEGSSKQAG","download_json":"https://pith.science/pith/SAARRDIDEOOXYZ6JBJEGSSKQAG.json","view_paper":"https://pith.science/paper/SAARRDID","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.06189&json=true","fetch_graph":"https://pith.science/api/pith-number/SAARRDIDEOOXYZ6JBJEGSSKQAG/graph.json","fetch_events":"https://pith.science/api/pith-number/SAARRDIDEOOXYZ6JBJEGSSKQAG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SAARRDIDEOOXYZ6JBJEGSSKQAG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SAARRDIDEOOXYZ6JBJEGSSKQAG/action/storage_attestation","attest_author":"https://pith.science/pith/SAARRDIDEOOXYZ6JBJEGSSKQAG/action/author_attestation","sign_citation":"https://pith.science/pith/SAARRDIDEOOXYZ6JBJEGSSKQAG/action/citation_signature","submit_replication":"https://pith.science/pith/SAARRDIDEOOXYZ6JBJEGSSKQAG/action/replication_record"}},"created_at":"2026-07-05T09:05:39.450854+00:00","updated_at":"2026-07-05T09:05:39.450854+00:00"}