{"id":"https://openalex.org/W7164825138","doi":"https://doi.org/10.1145/3805622.3810849","title":"AutoAWG: Adverse Weather Generation with Adaptive Multi-Controls for Automotive Videos","display_name":"AutoAWG: Adverse Weather Generation with Adaptive Multi-Controls for Automotive Videos","publication_year":2026,"publication_date":"2026-06-15","ids":{"openalex":"https://openalex.org/W7164825138","doi":"https://doi.org/10.1145/3805622.3810849"},"language":null,"primary_location":{"id":"doi:10.1145/3805622.3810849","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805622.3810849","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3805622.3810849","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072824455","display_name":"Jiagao Hu","orcid":"https://orcid.org/0000-0002-8439-8903"},"institutions":[{"id":"https://openalex.org/I91125648","display_name":"Wuhan Institute of Technology","ror":"https://ror.org/04jcykh16","country_code":"CN","type":"education","lineage":["https://openalex.org/I91125648"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiagao Hu","raw_affiliation_strings":["MiLM Plus, Xiaomi Inc., Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-8439-8903","affiliations":[{"raw_affiliation_string":"MiLM Plus, Xiaomi Inc., Wuhan, China","institution_ids":["https://openalex.org/I91125648"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137634606","display_name":"Daiguo Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I91125648","display_name":"Wuhan Institute of Technology","ror":"https://ror.org/04jcykh16","country_code":"CN","type":"education","lineage":["https://openalex.org/I91125648"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Daiguo Zhou","raw_affiliation_strings":["MiLM Plus, Xiaomi Inc., Wuhan, China"],"raw_orcid":"https://orcid.org/0009-0009-9351-2067","affiliations":[{"raw_affiliation_string":"MiLM Plus, Xiaomi Inc., Wuhan, China","institution_ids":["https://openalex.org/I91125648"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103207941","display_name":"Di Fu","orcid":"https://orcid.org/0000-0002-5385-2982"},"institutions":[{"id":"https://openalex.org/I91125648","display_name":"Wuhan Institute of Technology","ror":"https://ror.org/04jcykh16","country_code":"CN","type":"education","lineage":["https://openalex.org/I91125648"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Danzhen Fu","raw_affiliation_strings":["MiLM Plus, Xiaomi Inc., Wuhan, China"],"raw_orcid":"https://orcid.org/0009-0000-2259-9171","affiliations":[{"raw_affiliation_string":"MiLM Plus, Xiaomi Inc., Wuhan, China","institution_ids":["https://openalex.org/I91125648"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089436024","display_name":"Fuhao Li","orcid":"https://orcid.org/0009-0003-4830-2171"},"institutions":[{"id":"https://openalex.org/I91125648","display_name":"Wuhan Institute of Technology","ror":"https://ror.org/04jcykh16","country_code":"CN","type":"education","lineage":["https://openalex.org/I91125648"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fuhao Li","raw_affiliation_strings":["MiLM Plus, Xiaomi Inc., Wuhan, China"],"raw_orcid":"https://orcid.org/0009-0003-4830-2171","affiliations":[{"raw_affiliation_string":"MiLM Plus, Xiaomi Inc., Wuhan, China","institution_ids":["https://openalex.org/I91125648"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138654875","display_name":"Zepeng Wang","orcid":"https://orcid.org/0000-0002-2366-8455"},"institutions":[{"id":"https://openalex.org/I91125648","display_name":"Wuhan Institute of Technology","ror":"https://ror.org/04jcykh16","country_code":"CN","type":"education","lineage":["https://openalex.org/I91125648"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zepeng Wang","raw_affiliation_strings":["MiLM Plus, Xiaomi Inc., Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-2366-8455","affiliations":[{"raw_affiliation_string":"MiLM Plus, Xiaomi Inc., Wuhan, China","institution_ids":["https://openalex.org/I91125648"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100455804","display_name":"Fei Wang","orcid":"https://orcid.org/0000-0002-2143-9421"},"institutions":[{"id":"https://openalex.org/I91125648","display_name":"Wuhan Institute of Technology","ror":"https://ror.org/04jcykh16","country_code":"CN","type":"education","lineage":["https://openalex.org/I91125648"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Wang","raw_affiliation_strings":["MiLM Plus, Xiaomi Inc., Wuhan, China"],"raw_orcid":"https://orcid.org/0009-0009-5698-396X","affiliations":[{"raw_affiliation_string":"MiLM Plus, Xiaomi Inc., Wuhan, China","institution_ids":["https://openalex.org/I91125648"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101999989","display_name":"Wenhua Liao","orcid":"https://orcid.org/0000-0002-9422-9484"},"institutions":[{"id":"https://openalex.org/I91125648","display_name":"Wuhan Institute of Technology","ror":"https://ror.org/04jcykh16","country_code":"CN","type":"education","lineage":["https://openalex.org/I91125648"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenhua Liao","raw_affiliation_strings":["MiLM Plus, Xiaomi Inc., Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-9422-9484","affiliations":[{"raw_affiliation_string":"MiLM Plus, Xiaomi Inc., Wuhan, China","institution_ids":["https://openalex.org/I91125648"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100928892","display_name":"Jiayi Xie","orcid":"https://orcid.org/0000-0002-8712-326X"},"institutions":[{"id":"https://openalex.org/I91125648","display_name":"Wuhan Institute of Technology","ror":"https://ror.org/04jcykh16","country_code":"CN","type":"education","lineage":["https://openalex.org/I91125648"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiayi Xie","raw_affiliation_strings":["MiLM Plus, Xiaomi Inc., Wuhan, China"],"raw_orcid":"https://orcid.org/0009-0002-6586-219X","affiliations":[{"raw_affiliation_string":"MiLM Plus, Xiaomi Inc., Wuhan, China","institution_ids":["https://openalex.org/I91125648"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5138642481","display_name":"Haiyang Sun","orcid":"https://orcid.org/0009-0008-5252-0696"},"institutions":[{"id":"https://openalex.org/I4210086486","display_name":"Zhaotong University","ror":"https://ror.org/00264zf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210086486"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haiyang Sun","raw_affiliation_strings":["Xiaomi EV, Shaighai, China"],"raw_orcid":"https://orcid.org/0009-0008-5252-0696","affiliations":[{"raw_affiliation_string":"Xiaomi EV, Shaighai, China","institution_ids":["https://openalex.org/I4210086486"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":9,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.93593357,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"835","last_page":"844"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.8949999809265137,"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/T11019","display_name":"Image Enhancement Techniques","score":0.8949999809265137,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.061000000685453415,"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":0.008700000122189522,"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/adverse-weather","display_name":"Adverse weather","score":0.6840000152587891},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.6140999794006348},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6111000180244446},{"id":"https://openalex.org/keywords/automotive-industry","display_name":"Automotive industry","score":0.4542999863624573},{"id":"https://openalex.org/keywords/scarcity","display_name":"Scarcity","score":0.35199999809265137},{"id":"https://openalex.org/keywords/unexpected-events","display_name":"Unexpected events","score":0.328900009393692}],"concepts":[{"id":"https://openalex.org/C2992147540","wikidata":"https://www.wikidata.org/wiki/Q1277161","display_name":"Adverse weather","level":2,"score":0.6840000152587891},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6833000183105469},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6140999794006348},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6111000180244446},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.460099995136261},{"id":"https://openalex.org/C526921623","wikidata":"https://www.wikidata.org/wiki/Q190117","display_name":"Automotive industry","level":2,"score":0.4542999863624573},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3831999897956848},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.38269999623298645},{"id":"https://openalex.org/C109747225","wikidata":"https://www.wikidata.org/wiki/Q815758","display_name":"Scarcity","level":2,"score":0.35199999809265137},{"id":"https://openalex.org/C2776544517","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Unexpected events","level":2,"score":0.328900009393692},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.3086000084877014},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.27410000562667847},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3805622.3810849","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805622.3810849","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3805622.3810849","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805622.3810849","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W2340897893","https://openalex.org/W2748263833","https://openalex.org/W2963800716","https://openalex.org/W2963866045","https://openalex.org/W3035564946","https://openalex.org/W3035574168","https://openalex.org/W3174458495","https://openalex.org/W3183110203","https://openalex.org/W3207649350","https://openalex.org/W3215632849","https://openalex.org/W4200150166","https://openalex.org/W4312961318","https://openalex.org/W4313854933","https://openalex.org/W4317496742","https://openalex.org/W4383066393","https://openalex.org/W4385318467","https://openalex.org/W4385452935","https://openalex.org/W4386072291","https://openalex.org/W4390871691","https://openalex.org/W4390871801","https://openalex.org/W4402716256","https://openalex.org/W4402727021","https://openalex.org/W4402754225","https://openalex.org/W4402754263","https://openalex.org/W4402778069","https://openalex.org/W4404876506","https://openalex.org/W4409366191","https://openalex.org/W4411244900","https://openalex.org/W4413144911","https://openalex.org/W4413146585","https://openalex.org/W4413157439","https://openalex.org/W7103748842"],"related_works":[],"abstract_inverted_index":{"Perception":[0],"robustness":[1],"under":[2],"adverse":[3,24],"weather":[4,27,64],"remains":[5],"a":[6,41,54,73],"critical":[7],"challenge":[8],"for":[9,48,158],"autonomous":[10,163],"driving,":[11],"with":[12,66,125],"the":[13,17,102,153],"core":[14],"bottleneck":[15],"being":[16],"scarcity":[18],"of":[19,58,69,156],"real-world":[20],"video":[21,45],"data":[22],"in":[23,144,162],"weather.":[25],"Existing":[26],"generation":[28,99],"approaches":[29],"struggle":[30],"to":[31,61,79,96],"balance":[32,62],"visual":[33],"quality":[34],"and":[35,92,116,123,134,139,149],"annotation":[36],"reusability.":[37],"We":[38],"present":[39],"AutoAWG,":[40],"controllable":[42],"Adverse":[43],"Weather":[44],"Generation":[46],"framework":[47],"Autonomous":[49],"driving.":[50,164],"Our":[51,165],"method":[52],"employs":[53],"semantics-guided":[55],"adaptive":[56],"fusion":[57],"multiple":[59],"controls":[60],"strong":[63],"stylization":[65],"high-fidelity":[67],"preservation":[68],"safety-critical":[70],"targets;":[71],"leverages":[72],"vanishing":[74],"point-anchored":[75],"temporal":[76,147],"synthesis":[77],"strategy":[78],"construct":[80],"training":[81,95],"sequences":[82],"from":[83],"static":[84],"images,":[85],"thereby":[86],"reducing":[87],"reliance":[88],"on":[89],"synthetic":[90],"data;":[91],"adopts":[93],"masked":[94],"enhance":[97],"long-horizon":[98],"stability.":[100],"On":[101],"nuScenes":[103],"validation":[104],"set,":[105],"AutoAWG":[106,157],"significantly":[107],"outperforms":[108],"prior":[109],"state-of-the-art":[110],"methods:":[111],"without":[112],"first-frame":[113,126],"conditioning,":[114,127],"FID":[115],"FVD":[117],"are":[118,129],"relatively":[119],"reduced":[120,131],"by":[121,132],"50.0%":[122],"16.1%;":[124],"they":[128],"further":[130],"8.7%":[133],"7.2%,":[135],"respectively.":[136],"Extensive":[137],"qualitative":[138],"quantitative":[140],"results":[141],"demonstrate":[142],"advantages":[143],"style":[145],"fidelity,":[146],"consistency,":[148],"semantic\u2013structural":[150],"integrity,":[151],"underscoring":[152],"practical":[154],"value":[155],"improving":[159],"downstream":[160],"perception":[161],"code":[166],"is":[167],"available":[168],"at:":[169],"https://github.com/higherhu/AutoAWG":[170]},"counts_by_year":[],"updated_date":"2026-06-16T07:37:23.134862","created_date":"2026-06-16T00:00:00"}
