{"id":"https://openalex.org/W7140752412","doi":"https://doi.org/10.48550/arxiv.2603.24541","title":"SEGAR: Selective Enhancement for Generative Augmented Reality","display_name":"SEGAR: Selective Enhancement for Generative Augmented Reality","publication_year":2026,"publication_date":"2026-03-25","ids":{"openalex":"https://openalex.org/W7140752412","doi":"https://doi.org/10.48550/arxiv.2603.24541"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.24541","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24541","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.24541","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5060098545","display_name":"Fanjun Bu","orcid":"https://orcid.org/0000-0002-9953-7347"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bu, Fanjun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005632055","display_name":"Chenyang Yuan","orcid":"https://orcid.org/0000-0001-7471-6114"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan, Chenyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130649607","display_name":"Hiroshi Yasuda","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yasuda, Hiroshi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"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":0.6121000051498413,"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":0.6121000051498413,"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/T10888","display_name":"Augmented Reality Applications","score":0.061500001698732376,"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.05570000037550926,"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/generative-grammar","display_name":"Generative grammar","score":0.6876000165939331},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.6481999754905701},{"id":"https://openalex.org/keywords/augmented-reality","display_name":"Augmented reality","score":0.5515999794006348},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.546500027179718},{"id":"https://openalex.org/keywords/real-world-data","display_name":"Real world data","score":0.4494999945163727},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.44530001282691956}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7537999749183655},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6876000165939331},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.6481999754905701},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6161999702453613},{"id":"https://openalex.org/C153715457","wikidata":"https://www.wikidata.org/wiki/Q254183","display_name":"Augmented reality","level":2,"score":0.5515999794006348},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.546500027179718},{"id":"https://openalex.org/C3020493868","wikidata":"https://www.wikidata.org/wiki/Q55631277","display_name":"Real world data","level":2,"score":0.4494999945163727},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.44530001282691956},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38420000672340393},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.3635999858379364},{"id":"https://openalex.org/C2781235140","wikidata":"https://www.wikidata.org/wiki/Q275131","display_name":"Scratch","level":2,"score":0.3440000116825104},{"id":"https://openalex.org/C173552908","wikidata":"https://www.wikidata.org/wiki/Q1366289","display_name":"Graphics pipeline","level":4,"score":0.3181000053882599},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3000999987125397}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.24541","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24541","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.24541","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24541","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":"article"},"sustainable_development_goals":[{"score":0.6588829755783081,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Generative":[0],"world":[1,58,70,131],"models":[2,132],"offer":[3],"a":[4,51,56,61,106],"compelling":[5],"foundation":[6],"for":[7],"augmented-reality":[8],"(AR)":[9],"applications:":[10],"by":[11],"predicting":[12],"future":[13,26,74,138],"image":[14],"sequences":[15],"that":[16,28,54],"incorporate":[17],"deliberate":[18],"visual":[19],"edits,":[20],"they":[21],"enable":[22],"temporally":[23],"coherent,":[24],"augmented":[25,73],"frames":[27,75,139],"can":[29,140],"be":[30,141],"computed":[31],"ahead":[32],"of":[33],"time":[34],"and":[35,82,116,144],"cached,":[36,143],"avoiding":[37],"per-frame":[38],"rendering":[39],"from":[40],"scratch":[41],"in":[42,102],"real":[43],"time.":[44],"In":[45],"this":[46,67,100,124],"work,":[47],"we":[48],"present":[49],"SEGAR,":[50],"preliminary":[52],"framework":[53],"combines":[55],"diffusion-based":[57],"model":[59,71],"with":[60,76,90],"selective":[62],"correction":[63,84],"stage":[64,85],"to":[65],"support":[66],"vision.":[68],"The":[69],"generates":[72],"region-specific":[77],"edits":[78],"while":[79,93],"preserving":[80,94],"others,":[81],"the":[83],"subsequently":[86],"aligns":[87],"safety-critical":[88],"regions":[89],"real-world":[91,117],"observations":[92],"intended":[95],"augmentations":[96],"elsewhere.":[97],"We":[98,122],"demonstrate":[99],"pipeline":[101],"driving":[103],"scenarios":[104],"as":[105,125,133],"representative":[107],"setting":[108],"where":[109,137],"semantic":[110],"region":[111],"structure":[112],"is":[113,119],"well":[114],"defined":[115],"feedback":[118],"readily":[120],"available.":[121],"view":[123],"an":[126],"early":[127],"step":[128],"toward":[129],"generative":[130],"practical":[134],"AR":[135],"infrastructure,":[136],"generated,":[142],"selectively":[145],"corrected":[146],"on":[147],"demand.":[148]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-27T00:00:00"}
