{"id":"https://openalex.org/W7161178214","doi":"https://doi.org/10.48550/arxiv.2605.13755","title":"Generative Texture Diversification of 3D Pedestrians for Robust Autonomous Driving Perception","display_name":"Generative Texture Diversification of 3D Pedestrians for Robust Autonomous Driving Perception","publication_year":2026,"publication_date":"2026-05-13","ids":{"openalex":"https://openalex.org/W7161178214","doi":"https://doi.org/10.48550/arxiv.2605.13755"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.13755","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13755","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.2605.13755","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074442824","display_name":"Arka Bhowmick","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bhowmick, Arka","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119181765","display_name":"Enes \u00d6zeren","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ozeren, Enes","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136140369","display_name":"Ahmed Abdullah","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abdullah, Ahmed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5020732874","display_name":"Oliver Wasenm\u00fcller","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wasenmuller, Oliver","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":0.36629998683929443,"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":0.36629998683929443,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.329800009727478,"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":0.11150000244379044,"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/robustness","display_name":"Robustness (evolution)","score":0.570900022983551},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.5649999976158142},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5099999904632568},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.484499990940094},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.4519999921321869},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.43220001459121704},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.42480000853538513},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.4117000102996826}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7186999917030334},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6597999930381775},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.570900022983551},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.5649999976158142},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5099999904632568},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.484499990940094},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.4519999921321869},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.43220001459121704},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.42480000853538513},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4117000102996826},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.40470001101493835},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39899998903274536},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38019999861717224},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3328999876976013},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3273000121116638},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.31779998540878296},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C83248878","wikidata":"https://www.wikidata.org/wiki/Q344000","display_name":"Active appearance model","level":3,"score":0.2935999929904938},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.29019999504089355},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.28700000047683716},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C180916674","wikidata":"https://www.wikidata.org/wiki/Q3711935","display_name":"Diversification (marketing strategy)","level":2,"score":0.2709999978542328}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.13755","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13755","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.2605.13755","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13755","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0,68],"recent":[1],"years,":[2],"autonomous":[3,66,231],"driving":[4,232],"has":[5],"significantly":[6],"in":[7,65,81,169,187,230],"creased":[8],"the":[9,61,131,140,148,192,222],"demand":[10,31],"for":[11,21,78,85,136,156,173],"high-quality":[12],"data":[13,40,49,155],"to":[14,28,198],"train":[15],"2D":[16,188],"and":[17,37,44,53,107,113,146,153,224],"3D":[18,82,93,118,174,195],"perception":[19,172,196],"models":[20,197],"safety-critical":[22,63],"scenarios.":[23],"Real":[24],"world":[25],"datasets":[26,145],"struggle":[27],"meet":[29],"this":[30,69,203],"as":[32],"require":[33],"ments":[34],"continuously":[35],"evolve":[36],"large-scale":[38],"annotated":[39],"collection":[41],"remains":[42],"costly":[43],"time-consuming":[45],"making":[46],"syn":[47],"thetic":[48],"a":[50,73,91],"scalable,":[51,211],"practical":[52],"controllable":[54],"alterna":[55],"tive.":[56],"Pedestrian":[57],"detection":[58,189],"is":[59],"among":[60],"most":[62],"tasks":[64],"driving.":[67],"paper,":[70],"we":[71,96,142,164],"propose":[72],"simple":[74],"yet":[75],"effective":[76],"method":[77],"scaling":[79],"variability":[80],"pedestrian":[83,100,213],"assets":[84],"synthetic":[86,144,154,182],"scene":[87],"generation.":[88],"Starting":[89],"from":[90],"single":[92],"base":[94],"asset,":[95],"generate":[97],"multiple":[98],"distinct":[99],"instances":[101],"by":[102],"synthesizing":[103],"diverse":[104],"facial":[105,217],"textures":[106],"identity-level":[108],"appearance":[109],"variations":[110],"using":[111],"StyleGAN2":[112],"automatically":[114],"mapping":[115],"them":[116],"onto":[117],"meshes.":[119],"This":[120],"ap":[121],"proach":[122],"enables":[123],"scalable":[124],"appearance-level":[125],"asset":[126],"diversifica":[127,183],"tion":[128,184],"without":[129],"requiring":[130],"design":[132],"of":[133,150,194,226],"new":[134],"geometries":[135],"each":[137],"instance.":[138],"Using":[139],"assets,":[141],"construct":[143],"study":[147],"impact":[149],"mixing":[151],"real":[152],"RGB-based":[157],"object":[158,175],"detection.":[159,176],"Through":[160],"complementary":[161],"ex":[162],"periments,":[163],"analyze":[165],"geometry-driven":[166],"distribution":[167],"shifts":[168],"point":[170],"cloud":[171],"Our":[177],"findings":[178],"demonstrate":[179],"that":[180],"controlled":[181,216],"improves":[185],"robustness":[186],"while":[190],"revealing":[191],"sensitivity":[193],"geometric":[199],"domain":[200],"gaps.":[201],"Overall,":[202],"work":[204],"highlights":[205],"how":[206],"generative":[207],"AI":[208],"en":[209],"ables":[210],"simulation-ready":[212],"diversification":[214],"through":[215],"texture":[218],"synthesis,":[219],"along":[220],"with":[221],"benefits":[223],"limitations":[225],"cross-domain":[227],"training":[228],"strategies":[229],"pipelines.":[233]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-15T00:00:00"}