{"id":"https://openalex.org/W7161238460","doi":"https://doi.org/10.48550/arxiv.2605.14708","title":"StyleTextGen: Style-Conditioned Multilingual Scene Text Generation","display_name":"StyleTextGen: Style-Conditioned Multilingual Scene Text Generation","publication_year":2026,"publication_date":"2026-05-14","ids":{"openalex":"https://openalex.org/W7161238460","doi":"https://doi.org/10.48550/arxiv.2605.14708"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.14708","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14708","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2605.14708","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136196476","display_name":"Zeyu Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Zeyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101371226","display_name":"Fangmin Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Fangmin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136237262","display_name":"Yan Shu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shu, Yan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136239055","display_name":"Yichao Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yichao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107865080","display_name":"Liu Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136256536","display_name":"Yu Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Yu","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":0.607200026512146,"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":0.607200026512146,"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/T11574","display_name":"Artificial Intelligence in Games","score":0.1143999993801117,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.08990000188350677,"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/consistency","display_name":"Consistency (knowledge bases)","score":0.7592999935150146},{"id":"https://openalex.org/keywords/style","display_name":"Style (visual arts)","score":0.6931999921798706},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.47540000081062317},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4666000008583069},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.46549999713897705},{"id":"https://openalex.org/keywords/coherence","display_name":"Coherence (philosophical gambling strategy)","score":0.4472000002861023}],"concepts":[{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.7592999935150146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7473999857902527},{"id":"https://openalex.org/C2776445246","wikidata":"https://www.wikidata.org/wiki/Q1792644","display_name":"Style (visual arts)","level":2,"score":0.6931999921798706},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5698999762535095},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5309000015258789},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.47540000081062317},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4666000008583069},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.46549999713897705},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.4472000002861023},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.44600000977516174},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4408999979496002},{"id":"https://openalex.org/C13622073","wikidata":"https://www.wikidata.org/wiki/Q2243831","display_name":"Writing style","level":2,"score":0.3450999855995178},{"id":"https://openalex.org/C2781162219","wikidata":"https://www.wikidata.org/wiki/Q26250693","display_name":"Replicate","level":2,"score":0.3443000018596649},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.322299987077713},{"id":"https://openalex.org/C2985684807","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Text generation","level":2,"score":0.31929999589920044},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.27230000495910645}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.14708","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14708","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.14708","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14708","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.8548344969749451,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Style-conditioned":[0],"scene":[1,117],"text":[2,10,39,67,78,118,149],"generation":[3],"faces":[4],"unique":[5],"challenges":[6],"in":[7,70,136,146],"extracting":[8],"precise":[9,100],"styles":[11,40],"from":[12],"complex":[13,71],"backgrounds":[14],"and":[15,36,44,86,105,124,139],"maintaining":[16],"fine-grained":[17],"style":[18,58,62,68,79,84,101,119,137],"consistency":[19,80,138],"across":[20,41],"characters,":[21],"especially":[22],"for":[23],"multilingual":[24,66,147],"scripts.":[25],"We":[26],"propose":[27],"StyleTextGen,":[28],"a":[29,56,77,94,115],"novel":[30],"framework":[31],"that":[32,82,98,130],"learns":[33],"to":[34,61],"perceive":[35],"replicate":[37],"visual":[38,89],"different":[42],"languages":[43],"writing":[45],"systems.":[46],"Our":[47],"approach":[48],"features":[49],"three":[50],"key":[51],"contributions:":[52],"First,":[53],"we":[54,75,92,112],"introduce":[55],"dual-branch":[57],"encoder":[59],"dedicated":[60],"modeling,":[63],"yielding":[64],"robust":[65],"representations":[69],"real-world":[72],"scenes.":[73],"Second,":[74],"design":[76],"loss":[81],"enhances":[83],"coherence":[85],"improves":[87],"overall":[88],"quality.":[90],"Third,":[91],"develop":[93],"mask-guided":[95],"inference":[96],"strategy":[97],"ensures":[99],"alignment":[102],"between":[103],"generated":[104],"reference":[106],"text.":[107],"To":[108],"facilitate":[109],"systematic":[110],"evaluation,":[111],"construct":[113],"StyleText-CE,":[114],"bilingual":[116],"benchmark":[120],"covering":[121],"both":[122],"monolingual":[123],"cross-lingual":[125,140],"settings.":[126],"Extensive":[127],"experiments":[128],"demonstrate":[129],"StyleTextGen":[131],"significantly":[132],"outperforms":[133],"existing":[134],"methods":[135],"generalization,":[141],"establishing":[142],"new":[143],"state-of-the-art":[144],"performance":[145],"style-conditioned":[148],"generation.":[150]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-16T00:00:00"}