{"id":"https://openalex.org/W3009442731","doi":"https://doi.org/10.1109/wacv45572.2020.9093443","title":"Learn a Global Appearance Semi-Supervisedly for Synthesizing Person Images","display_name":"Learn a Global Appearance Semi-Supervisedly for Synthesizing Person Images","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3009442731","doi":"https://doi.org/10.1109/wacv45572.2020.9093443","mag":"3009442731"},"language":"en","primary_location":{"id":"doi:10.1109/wacv45572.2020.9093443","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093443","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075795070","display_name":"Zhipeng Ge","orcid":"https://orcid.org/0000-0002-4053-4504"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhipeng Ge","raw_affiliation_strings":["Nanjing University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100405433","display_name":"Fei Chen","orcid":"https://orcid.org/0000-0003-2482-2204"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Chen","raw_affiliation_strings":["Nanjing University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046137144","display_name":"Sidan Du","orcid":"https://orcid.org/0000-0001-6432-3704"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sidan Du","raw_affiliation_strings":["Nanjing University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101505594","display_name":"Yao Yu","orcid":"https://orcid.org/0000-0002-4933-1383"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yao Yu","raw_affiliation_strings":["Nanjing University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038150414","display_name":"Yu Zhou","orcid":"https://orcid.org/0000-0002-3723-7584"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Zhou","raw_affiliation_strings":["Nanjing University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I881766915"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02305594,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"abs 1612 3242","issue":null,"first_page":"1179","last_page":"1188"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9997000098228455,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9997000098228455,"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":0.9995999932289124,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9990000128746033,"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/computer-science","display_name":"Computer science","score":0.8044581413269043},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6728770732879639},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics","score":0.648762583732605},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.617772102355957},{"id":"https://openalex.org/keywords/graphics","display_name":"Graphics","score":0.5649794936180115},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5012915134429932},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.47885748744010925},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.4597243666648865},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3831421732902527},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3338172435760498},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.22058191895484924}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8044581413269043},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6728770732879639},{"id":"https://openalex.org/C77660652","wikidata":"https://www.wikidata.org/wiki/Q150971","display_name":"Computer graphics","level":2,"score":0.648762583732605},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.617772102355957},{"id":"https://openalex.org/C21442007","wikidata":"https://www.wikidata.org/wiki/Q1027879","display_name":"Graphics","level":2,"score":0.5649794936180115},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5012915134429932},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.47885748744010925},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.4597243666648865},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3831421732902527},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3338172435760498},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.22058191895484924},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv45572.2020.9093443","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093443","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W1154498256","https://openalex.org/W1578285471","https://openalex.org/W1959608418","https://openalex.org/W1967554269","https://openalex.org/W2099471712","https://openalex.org/W2101032778","https://openalex.org/W2108598243","https://openalex.org/W2125389028","https://openalex.org/W2133665775","https://openalex.org/W2194775991","https://openalex.org/W2204750386","https://openalex.org/W2331128040","https://openalex.org/W2348664362","https://openalex.org/W2405756170","https://openalex.org/W2471768434","https://openalex.org/W2519536754","https://openalex.org/W2559655401","https://openalex.org/W2739748921","https://openalex.org/W2751023760","https://openalex.org/W2771558241","https://openalex.org/W2791184993","https://openalex.org/W2798714868","https://openalex.org/W2798777978","https://openalex.org/W2890816492","https://openalex.org/W2899882700","https://openalex.org/W2962793481","https://openalex.org/W2962819541","https://openalex.org/W2962963674","https://openalex.org/W2962982136","https://openalex.org/W2963073614","https://openalex.org/W2963226019","https://openalex.org/W2963338719","https://openalex.org/W2963373786","https://openalex.org/W2963522749","https://openalex.org/W2963630103","https://openalex.org/W2963734522","https://openalex.org/W2963784072","https://openalex.org/W2963800363","https://openalex.org/W2963876278","https://openalex.org/W2963995996","https://openalex.org/W2964024144","https://openalex.org/W2964121744","https://openalex.org/W2984529706","https://openalex.org/W4289744708","https://openalex.org/W4301054805","https://openalex.org/W6702130928","https://openalex.org/W6704970058","https://openalex.org/W6713645886","https://openalex.org/W6718140377","https://openalex.org/W6718379498","https://openalex.org/W6720691552","https://openalex.org/W6729966448","https://openalex.org/W6730746255","https://openalex.org/W6738824914","https://openalex.org/W6748645028","https://openalex.org/W6753166803","https://openalex.org/W6755912606","https://openalex.org/W6770076868"],"related_works":["https://openalex.org/W2888032422","https://openalex.org/W2996316059","https://openalex.org/W4377980832","https://openalex.org/W2897769091","https://openalex.org/W2845413374","https://openalex.org/W3005996785","https://openalex.org/W4297411772","https://openalex.org/W4283758926","https://openalex.org/W4235873501","https://openalex.org/W4393270992"],"abstract_inverted_index":{"We":[0,94],"present":[1],"a":[2,75,112,125,140,163],"novel":[3],"approach":[4,172],"for":[5,177],"person":[6,15,69,174],"images":[7,16,70,175],"synthesis":[8,176],"in":[9,17,30,62,74,79],"this":[10],"paper,":[11],"that":[12],"can":[13,43,66,83,138],"generate":[14,67],"arbitrary":[18],"poses,":[19,58],"shapes":[20,59,87],"and":[21,50,60,162],"views.":[22],"Unlike":[23],"existing":[24],"methods":[25],"just":[26],"using":[27],"keypoints'":[28],"locations":[29],"heatmaps":[31],"format,":[32],"we":[33,65,82,115,137],"propose":[34],"to":[35,39,98],"render":[36],"SMPL":[37,63,108],"model":[38],"UV":[40],"maps,":[41],"which":[42],"provide":[44],"human":[45],"structural":[46],"information":[47],"about":[48],"poses":[49],"shapes.":[51],"Thus,":[52],"by":[53,90,153],"varying":[54],"the":[55,129,133,144,149,167],"parameters":[56,109],"of":[57,88,132,143,170],"camera":[61],"model,":[64,114],"different":[68,178],"with":[71,103],"various":[72],"attributions":[73],"simple":[76],"way,":[77],"while":[78],"most":[80],"cases":[81],"only":[84],"obtain":[85],"new":[86],"people":[89],"computer":[91],"graphics":[92],"methods.":[93],"train":[95],"an":[96],"end":[97,99],"generative":[100],"adversarial":[101],"network":[102,119,123],"unlabeled":[104],"data.":[105],"As":[106],"our":[107,117,171],"come":[110],"from":[111],"pretrained":[113],"call":[116],"overall":[118],"semi-":[120],"supervised.":[121],"Our":[122],"keeps":[124],"global":[126],"appearance":[127,142,151],"during":[128],"fine-tuning":[130],"stage":[131],"target":[134,145],"person,":[135,146],"thus":[136],"get":[139],"complete":[141],"rather":[147],"than":[148],"inaccurate":[150],"caused":[152],"inferencing":[154],"without":[155],"enough":[156],"information.":[157],"Experiments":[158],"on":[159,173],"Human3.6M":[160],"Dataset":[161],"self-collected":[164],"dataset":[165],"demonstrate":[166],"excellent":[168],"effectiveness":[169],"applications.":[179]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
