{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:34XB6TJBRL6YQOYBMQL46MSBQ3","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"cfa25d94ee8b15160451c3bc762b3243070c77481ff09563d16f7bfc04215ac4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-01-17T08:30:32Z","title_canon_sha256":"ea88f873525b178fd13417ee988aec5d02a674b99e35b7e5011dc4a538cc2bf6"},"schema_version":"1.0","source":{"id":"2401.09047","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.09047","created_at":"2026-07-05T07:34:38Z"},{"alias_kind":"arxiv_version","alias_value":"2401.09047v1","created_at":"2026-07-05T07:34:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.09047","created_at":"2026-07-05T07:34:38Z"},{"alias_kind":"pith_short_12","alias_value":"34XB6TJBRL6Y","created_at":"2026-07-05T07:34:38Z"},{"alias_kind":"pith_short_16","alias_value":"34XB6TJBRL6YQOYB","created_at":"2026-07-05T07:34:38Z"},{"alias_kind":"pith_short_8","alias_value":"34XB6TJB","created_at":"2026-07-05T07:34:38Z"}],"graph_snapshots":[{"event_id":"sha256:8c03dd803f6b01b17ebc3501071da9c291efad320afaf6158ec275b5b40154d3","target":"graph","created_at":"2026-07-05T07:34:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2401.09047/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Text-to-video generation aims to produce a video based on a given prompt. Recently, several commercial video models have been able to generate plausible videos with minimal noise, excellent details, and high aesthetic scores. However, these models rely on large-scale, well-filtered, high-quality videos that are not accessible to the community. Many existing research works, which train models using the low-quality WebVid-10M dataset, struggle to generate high-quality videos because the models are optimized to fit WebVid-10M. In this work, we explore the training scheme of video models extended ","authors_text":"Chao Weng, Haoxin Chen, Menghan Xia, Xiaodong Cun, Xintao Wang, Ying Shan, Yong Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-01-17T08:30:32Z","title":"VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.09047","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2fe3470beb0466d6a23fb211f47134ac0daa4870ee7aa6de9a2f3129f501d176","target":"record","created_at":"2026-07-05T07:34:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"cfa25d94ee8b15160451c3bc762b3243070c77481ff09563d16f7bfc04215ac4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-01-17T08:30:32Z","title_canon_sha256":"ea88f873525b178fd13417ee988aec5d02a674b99e35b7e5011dc4a538cc2bf6"},"schema_version":"1.0","source":{"id":"2401.09047","kind":"arxiv","version":1}},"canonical_sha256":"df2e1f4d218afd883b016417cf324186e1a09928c843fba028ab5aaff4ae6139","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"df2e1f4d218afd883b016417cf324186e1a09928c843fba028ab5aaff4ae6139","first_computed_at":"2026-07-05T07:34:38.745927Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:34:38.745927Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XCqPd6+lNWdozgrqRnuMojJ/Ba0Ol/rYw+zd5wPShQJA2+vWDC0YR5yCOHwEzlkaLvV5FjmP3wOQbvCMkLbOBA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:34:38.746362Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.09047","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2fe3470beb0466d6a23fb211f47134ac0daa4870ee7aa6de9a2f3129f501d176","sha256:8c03dd803f6b01b17ebc3501071da9c291efad320afaf6158ec275b5b40154d3"],"state_sha256":"6e5a0c8ce9a1f9e3bcbeab7822c47ac5659ce69470dec3ea3ba670a6be293aa2"}