{"id":"https://openalex.org/W7165777338","doi":"https://doi.org/10.48550/arxiv.2606.24336","title":"TIGER: Taming Identity, Geometry, and Generative Priors for High-Quality Face Video Restoration","display_name":"TIGER: Taming Identity, Geometry, and Generative Priors for High-Quality Face Video Restoration","publication_year":2026,"publication_date":"2026-06-23","ids":{"openalex":"https://openalex.org/W7165777338","doi":"https://doi.org/10.48550/arxiv.2606.24336"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.24336","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24336","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2606.24336","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139224828","display_name":"Yang Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065448472","display_name":"Wenxue Li","orcid":"https://orcid.org/0000-0002-1301-4933"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Wenxue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056795737","display_name":"P Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Peng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139255539","display_name":"Yifei Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yifei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029787563","display_name":"Fang Wang","orcid":"https://orcid.org/0000-0002-3327-4177"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Fei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139266845","display_name":"Daiguo Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Daiguo","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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.654699981212616,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.654699981212616,"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/T11448","display_name":"Face recognition and analysis","score":0.13369999825954437,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.11800000071525574,"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/prior-probability","display_name":"Prior probability","score":0.7457000017166138},{"id":"https://openalex.org/keywords/identity","display_name":"Identity (music)","score":0.6571000218391418},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.570900022983551},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.4902999997138977},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4864000082015991},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.44209998846054077},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.43650001287460327},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.3986000120639801}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.7457000017166138},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7127000093460083},{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.6571000218391418},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6215999722480774},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.570900022983551},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.4902999997138977},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4864000082015991},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.44209998846054077},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4399999976158142},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.43650001287460327},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.3986000120639801},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37220001220703125},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.359499990940094},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3257000148296356},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3237999975681305},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.29019999504089355},{"id":"https://openalex.org/C83248878","wikidata":"https://www.wikidata.org/wiki/Q344000","display_name":"Active appearance model","level":3,"score":0.26570001244544983},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.2630000114440918},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.2549000084400177}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.24336","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24336","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2606.24336","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24336","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.654219925403595}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Face":[0],"Video":[1],"Restoration":[2],"(FVR)":[3],"aims":[4],"to":[5,25,84,118,156,167],"recover":[6],"high-fidelity":[7],"facial":[8],"videos":[9],"from":[10],"degraded":[11],"input":[12],"while":[13],"preserving":[14],"identity":[15,31,79,184],"and":[16,35,54,153,171,186,193],"semantic":[17],"consistency":[18],"across":[19],"frames.":[20],"Existing":[21],"methods":[22],"often":[23],"struggle":[24],"simultaneously":[26],"address":[27],"three":[28],"key":[29],"challenges:":[30],"shift,":[32],"viewpoint-entangled":[33],"guidance,":[34],"perceptual":[36],"realism.":[37],"To":[38],"tackle":[39],"these":[40],"issues,":[41],"we":[42,125],"propose":[43],"TIGER,":[44],"a":[45,95,104,110,134,141,163,190],"structured":[46],"tri-prior":[47],"fusion":[48],"framework":[49],"that":[50,147,177],"Tames":[51],"Identity,":[52],"Geometry,":[53],"gEnerative":[55],"pRiors":[56],"for":[57,90],"high-quality":[58],"FVR.":[59,195],"Specifically,":[60],"an":[61],"Identity":[62],"Prior":[63,97,132],"is":[64],"first":[65],"established":[66],"by":[67,98],"injecting":[68],"subject-discriminative":[69],"embeddings":[70],"into":[71,103],"the":[72,77,127],"latent":[73],"space,":[74,108],"effectively":[75],"anchoring":[76],"subject's":[78],"against":[80],"severe":[81],"degradations.":[82],"Then,":[83],"provide":[85],"temporally":[86],"consistent":[87],"structural":[88,149],"guidance":[89],"dynamic":[91],"videos,":[92],"TIGER":[93,178],"constructs":[94],"Geometry":[96],"lifting":[99],"2D":[100],"reference":[101],"cues":[102],"disentangled":[105],"3D":[106],"parameter":[107,115],"creating":[109],"geometric":[111],"anchor":[112],"through":[113,133],"cross-source":[114],"fusion.":[116],"Moreover,":[117],"achieve":[119],"maximum":[120],"efficiency":[121],"without":[122],"compromising":[123],"realism,":[124],"harness":[126],"video":[128],"generation":[129],"model's":[130],"Generative":[131],"one-step":[135],"rectified":[136],"flow.":[137],"We":[138,160],"further":[139],"design":[140],"progressive":[142],"three-stage":[143],"training":[144,170],"optimization":[145],"strategy":[146],"refines":[148],"fidelity,":[150],"textural":[151],"reconstruction,":[152],"distribution-level":[154],"realism":[155],"ensure":[157],"robust":[158,169],"optimization.":[159],"also":[161],"construct":[162],"large-scale":[164],"FVR":[165],"dataset":[166],"facilitate":[168],"standardized":[172],"evaluation.":[173],"Extensive":[174],"experiments":[175],"demonstrate":[176],"achieves":[179],"state-of-the-art":[180],"performance":[181],"in":[182],"both":[183],"fidelity":[185],"temporal":[187],"stability,":[188],"delivering":[189],"high-quality,":[191],"efficient":[192],"identity-consistent":[194],"Project":[196],"page:":[197],"https://yzhoulv.github.io/Tiger/.":[198]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-25T00:00:00"}
