{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:BNWLEHSOGZ3DWOIIKVVPUK4NPN","short_pith_number":"pith:BNWLEHSO","schema_version":"1.0","canonical_sha256":"0b6cb21e4e36763b3908556afa2b8d7b684008ee52753303fb28ae91a44cb322","source":{"kind":"arxiv","id":"2407.21408","version":2},"attestation_state":"computed","paper":{"title":"Benchmarking Multi-dimensional AIGC Video Quality Assessment: A Dataset and Unified Model","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chunyi Li, Fengyu Sun, Guangtao Zhai, Jun Jia, Puyi Wang, Shangling Jui, Wei Sun, Xinyue Li, Xiongkuo Min, Zhichao Zhang, Zicheng Zhang, Zijian Chen","submitted_at":"2024-07-31T07:54:26Z","abstract_excerpt":"In recent years, artificial intelligence (AI)-driven video generation has gained significant attention. Consequently, there is a growing need for accurate video quality assessment (VQA) metrics to evaluate the perceptual quality of AI-generated content (AIGC) videos and optimize video generation models. However, assessing the quality of AIGC videos remains a significant challenge because these videos often exhibit highly complex distortions, such as unnatural actions and irrational objects. To address this challenge, we systematically investigate the AIGC-VQA problem, considering both subjecti"},"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":"2407.21408","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-07-31T07:54:26Z","cross_cats_sorted":[],"title_canon_sha256":"18a06bd0c94c2cbb2f2493a7bacb87cd49999873c60ed28fac3fa8410bea4efe","abstract_canon_sha256":"801010777d06b48fe6d699e50a180f6c9bf8583c7f632766bc1dcd198b614933"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:54:03.397905Z","signature_b64":"hZz4s8eLF6OrTiy/fxByFcitmm3QjYdAhpO59s9VKTieh1n7IzUhY3suIo3KTLRiU6Qbru4VRzQGq9XdFx8hBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b6cb21e4e36763b3908556afa2b8d7b684008ee52753303fb28ae91a44cb322","last_reissued_at":"2026-07-05T09:54:03.397412Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:54:03.397412Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Benchmarking Multi-dimensional AIGC Video Quality Assessment: A Dataset and Unified Model","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chunyi Li, Fengyu Sun, Guangtao Zhai, Jun Jia, Puyi Wang, Shangling Jui, Wei Sun, Xinyue Li, Xiongkuo Min, Zhichao Zhang, Zicheng Zhang, Zijian Chen","submitted_at":"2024-07-31T07:54:26Z","abstract_excerpt":"In recent years, artificial intelligence (AI)-driven video generation has gained significant attention. Consequently, there is a growing need for accurate video quality assessment (VQA) metrics to evaluate the perceptual quality of AI-generated content (AIGC) videos and optimize video generation models. However, assessing the quality of AIGC videos remains a significant challenge because these videos often exhibit highly complex distortions, such as unnatural actions and irrational objects. To address this challenge, we systematically investigate the AIGC-VQA problem, considering both subjecti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.21408","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/2407.21408/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":"2407.21408","created_at":"2026-07-05T09:54:03.397476+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.21408v2","created_at":"2026-07-05T09:54:03.397476+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.21408","created_at":"2026-07-05T09:54:03.397476+00:00"},{"alias_kind":"pith_short_12","alias_value":"BNWLEHSOGZ3D","created_at":"2026-07-05T09:54:03.397476+00:00"},{"alias_kind":"pith_short_16","alias_value":"BNWLEHSOGZ3DWOII","created_at":"2026-07-05T09:54:03.397476+00:00"},{"alias_kind":"pith_short_8","alias_value":"BNWLEHSO","created_at":"2026-07-05T09:54:03.397476+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2504.17180","citing_title":"We'll Fix it in Post: Improving Text-to-Video Generation with Neuro-Symbolic Feedback","ref_index":99,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BNWLEHSOGZ3DWOIIKVVPUK4NPN","json":"https://pith.science/pith/BNWLEHSOGZ3DWOIIKVVPUK4NPN.json","graph_json":"https://pith.science/api/pith-number/BNWLEHSOGZ3DWOIIKVVPUK4NPN/graph.json","events_json":"https://pith.science/api/pith-number/BNWLEHSOGZ3DWOIIKVVPUK4NPN/events.json","paper":"https://pith.science/paper/BNWLEHSO"},"agent_actions":{"view_html":"https://pith.science/pith/BNWLEHSOGZ3DWOIIKVVPUK4NPN","download_json":"https://pith.science/pith/BNWLEHSOGZ3DWOIIKVVPUK4NPN.json","view_paper":"https://pith.science/paper/BNWLEHSO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.21408&json=true","fetch_graph":"https://pith.science/api/pith-number/BNWLEHSOGZ3DWOIIKVVPUK4NPN/graph.json","fetch_events":"https://pith.science/api/pith-number/BNWLEHSOGZ3DWOIIKVVPUK4NPN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BNWLEHSOGZ3DWOIIKVVPUK4NPN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BNWLEHSOGZ3DWOIIKVVPUK4NPN/action/storage_attestation","attest_author":"https://pith.science/pith/BNWLEHSOGZ3DWOIIKVVPUK4NPN/action/author_attestation","sign_citation":"https://pith.science/pith/BNWLEHSOGZ3DWOIIKVVPUK4NPN/action/citation_signature","submit_replication":"https://pith.science/pith/BNWLEHSOGZ3DWOIIKVVPUK4NPN/action/replication_record"}},"created_at":"2026-07-05T09:54:03.397476+00:00","updated_at":"2026-07-05T09:54:03.397476+00:00"}