{"id":"https://openalex.org/W7139957600","doi":"https://doi.org/10.48550/arxiv.2603.19121","title":"CustomTex: High-fidelity Indoor Scene Texturing via Multi-Reference Customization","display_name":"CustomTex: High-fidelity Indoor Scene Texturing via Multi-Reference Customization","publication_year":2026,"publication_date":"2026-03-19","ids":{"openalex":"https://openalex.org/W7139957600","doi":"https://doi.org/10.48550/arxiv.2603.19121"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.19121","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19121","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.2603.19121","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130251578","display_name":"Weilin Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Weilin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130221150","display_name":"Jiahao Rao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rao, Jiahao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130245722","display_name":"Wenhao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Wenhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130248638","display_name":"Xinyang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xinyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130249666","display_name":"Xuan Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Xuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130215054","display_name":"Liujuan Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Liujuan","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":0.560699999332428,"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":0.560699999332428,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.22930000722408295,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.1177000030875206,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/set","display_name":"Set (abstract data type)","score":0.5873000025749207},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5519000291824341},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.44589999318122864},{"id":"https://openalex.org/keywords/texture-mapping","display_name":"Texture mapping","score":0.44339999556541443},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.42800000309944153},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.40939998626708984},{"id":"https://openalex.org/keywords/texture","display_name":"Texture (cosmology)","score":0.4081999957561493}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7871000170707703},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7580000162124634},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6923999786376953},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5873000025749207},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5519000291824341},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.44589999318122864},{"id":"https://openalex.org/C200585589","wikidata":"https://www.wikidata.org/wiki/Q752176","display_name":"Texture mapping","level":2,"score":0.44339999556541443},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.42800000309944153},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.40939998626708984},{"id":"https://openalex.org/C2781195486","wikidata":"https://www.wikidata.org/wiki/Q289436","display_name":"Texture (cosmology)","level":3,"score":0.4081999957561493},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.38909998536109924},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.37790000438690186},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3319999873638153},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.29750001430511475},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.27219998836517334},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.19121","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19121","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.2603.19121","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19121","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,83],"creation":[1],"of":[2,65,85],"high-fidelity,":[3],"customizable":[4,172],"3D":[5,60,173],"indoor":[6],"scene":[7,50,61,174],"textures":[8,29,147],"remains":[9],"a":[10,44,63,78,89,127,164],"significant":[11],"challenge.":[12],"While":[13],"text-driven":[14],"methods":[15],"offer":[16],"flexibility,":[17],"they":[18],"lack":[19],"the":[20,69],"precision":[21],"for":[22,47,72],"fine-grained,":[23],"instance-level":[24,140],"control,":[25],"and":[26,34,62,76,112,115,145,153,167],"often":[27],"produce":[28],"with":[30,104,142,148],"insufficient":[31],"quality,":[32],"artifacts,":[33,152],"baked-in":[35,155],"shading.":[36],"To":[37],"overcome":[38],"these":[39],"limitations,":[40],"we":[41],"introduce":[42],"CustomTex,":[43],"novel":[45],"framework":[46],"instance-level,":[48],"high-fidelity":[49],"texturing":[51],"driven":[52],"by":[53],"reference":[54,66,143],"images.":[55],"CustomTex":[56,137],"takes":[57],"an":[58,105],"untextured":[59],"set":[64],"images":[67,144],"specifying":[68],"desired":[70],"appearance":[71,175],"each":[73],"object":[74],"instance,":[75],"generates":[77],"unified,":[79],"high-resolution":[80],"texture":[81],"map.":[82],"core":[84],"our":[86],"method":[87],"is":[88],"dual-distillation":[90],"approach":[91],"that":[92,136],"separates":[93],"semantic":[94,110],"control":[95],"from":[96],"pixel-level":[97,116],"enhancement.":[98],"We":[99],"employ":[100],"semantic-level":[101],"distillation,":[102],"equipped":[103],"instance":[106],"cross-attention,":[107],"to":[108,118,158,170],"ensure":[109],"plausibility":[111],"``reference-instance''":[113],"alignment,":[114],"distillation":[117],"enforce":[119],"high":[120],"visual":[121],"fidelity.":[122],"Both":[123],"are":[124],"unified":[125],"within":[126],"Variational":[128],"Score":[129],"Distillation":[130],"(VSD)":[131],"optimization":[132],"framework.":[133],"Experiments":[134],"demonstrate":[135],"achieves":[138],"precise":[139],"consistency":[141],"produces":[146],"superior":[149],"sharpness,":[150],"reduced":[151],"minimal":[154],"shading":[156],"compared":[157],"state-of-the-art":[159],"methods.":[160],"Our":[161],"work":[162],"establishes":[163],"more":[165],"direct":[166],"user-friendly":[168],"path":[169],"high-quality,":[171],"editing.":[176]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-21T00:00:00"}