{"id":"https://openalex.org/W7169830951","doi":"https://doi.org/10.48550/arxiv.2607.16742","title":"Multi-Dimensional Quality Assessment for AI-Generated Human-Centric Videos: Dataset and Model","display_name":"Multi-Dimensional Quality Assessment for AI-Generated Human-Centric Videos: Dataset and Model","publication_year":2026,"publication_date":"2026-07-18","ids":{"openalex":"https://openalex.org/W7169830951","doi":"https://doi.org/10.48550/arxiv.2607.16742"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.16742","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.16742","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.16742","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5141253196","display_name":"Sijing Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Sijing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141304017","display_name":"Yunhao Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yunhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141290458","display_name":"Huiyu Duan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Duan, Huiyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141252307","display_name":"Yucheng Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Yucheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141255816","display_name":"Xiongkuo Min","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Min, Xiongkuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126492863","display_name":"Patrick Le Callet","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Callet, Patrick Le","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5141297000","display_name":"Guangtao Zhai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhai, Guangtao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":0.4260999858379364,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":0.4260999858379364,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.26809999346733093,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.042100001126527786,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.8406999707221985},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5770000219345093},{"id":"https://openalex.org/keywords/quality-assessment","display_name":"Quality assessment","score":0.5311999917030334},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.5109000205993652},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.45750001072883606},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3019999861717224}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.8406999707221985},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7325000166893005},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5770000219345093},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5430999994277954},{"id":"https://openalex.org/C3020001037","wikidata":"https://www.wikidata.org/wiki/Q836575","display_name":"Quality assessment","level":3,"score":0.5311999917030334},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5224000215530396},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.5109000205993652},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.45750001072883606},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3813000023365021},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2937999963760376},{"id":"https://openalex.org/C2777868144","wikidata":"https://www.wikidata.org/wiki/Q7239817","display_name":"Preference elicitation","level":3,"score":0.29030001163482666},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.28780001401901245},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C2779346075","wikidata":"https://www.wikidata.org/wiki/Q7268763","display_name":"Quality Score","level":3,"score":0.27709999680519104},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2540999948978424}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.16742","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.16742","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.16742","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.16742","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"AI-generated":[0,57,210],"human-centric":[1,58],"videos":[2,70],"play":[3],"a":[4,8,66,119,143,162],"crucial":[5],"role":[6],"in":[7,48,182,208],"wide":[9],"range":[10],"of":[11,27,150,156,167,200,221],"modern":[12],"applications.":[13],"However,":[14],"they":[15],"often":[16],"suffer":[17],"from":[18],"quality":[19,29,53,133,212],"issues":[20],"and":[21,77,87,99,122,138,154,172,188,193,204,214,219],"semantic":[22],"mismatches,":[23],"underscoring":[24],"the":[25,50,112,197,201,205,217],"importance":[26],"effective":[28],"assessment":[30,54,213],"for":[31,56],"such":[32],"videos.":[33],"To":[34],"this":[35],"end,":[36],"we":[37,115,147],"extend":[38],"our":[39],"previous":[40],"dataset":[41,55,203],"HVEval":[42],"with":[43,111,161],"pairwise":[44,136],"preference":[45,89],"annotations,":[46,80],"resulting":[47,181],"HVEval+,":[49],"largest":[51],"holistic":[52],"videos,":[59],"which":[60],"comprises":[61],"1k":[62],"prompts":[63],"based":[64],"on":[65,185],"comprehensive":[67,194],"taxonomy,":[68],"20k":[69,105],"generated":[71],"by":[72],"24":[73],"text-to-video":[74],"(T2V)":[75],"models,":[76],"extensive":[78],"human":[79],"including":[81],"60k":[82,88],"mean":[83],"opinion":[84],"scores":[85],"(MOSs)":[86],"pairs":[90],"across":[91],"3":[92],"dimensions":[93],"(i.e.,":[94],"spatial":[95],"quality,":[96,98],"temporal":[97],"text-video":[100],"correspondence),":[101],"as":[102,104],"well":[103],"category-specific":[106,139],"question-answer":[107],"(Q&A)":[108],"pairs.":[109],"Along":[110],"HVEval+":[113,187,202],"dataset,":[114],"further":[116,215],"propose":[117],"MoE-Rater,":[118],"Mixture-of-Experts":[120],"(MoE)-inspired":[121],"multimodal":[123],"large":[124],"language":[125],"model":[126],"(MLLM)-based":[127],"all-in-one":[128],"method":[129,207],"that":[130],"supports":[131],"multi-dimensional":[132,135],"rating,":[134],"comparison,":[137],"question":[140],"answering":[141],"within":[142],"single":[144],"model.":[145],"Specifically,":[146],"introduce":[148],"Mixture":[149,155],"Projector":[151],"Experts":[152,158],"(MoPE)":[153],"LoRA":[157],"(MoLE),":[159],"together":[160],"three-stage":[163],"training":[164],"strategy":[165],"consisting":[166],"task-aware":[168],"pre-training,":[169],"task-specific":[170],"adaptation,":[171],"adaptive":[173],"routing":[174],"optimization,":[175],"to":[176],"effectively":[177],"unify":[178],"multiple":[179],"tasks,":[180],"superior":[183],"performance":[184],"both":[186],"Human-AGVQA":[189],"datasets.":[190],"Extensive":[191],"experiments":[192],"analysis":[195],"demonstrate":[196],"significant":[198],"potential":[199],"MoE-Rater":[206],"advancing":[209],"video":[211],"facilitating":[216],"evaluation":[218],"optimization":[220],"T2V":[222],"models.":[223]},"counts_by_year":[],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2026-07-22T00:00:00"}
