{"id":"https://openalex.org/W7126061587","doi":"https://doi.org/10.1016/j.icte.2026.01.013","title":"Robust 3D pose estimation via occlusion-aware training and test time adaptation","display_name":"Robust 3D pose estimation via occlusion-aware training and test time adaptation","publication_year":2026,"publication_date":"2026-01-29","ids":{"openalex":"https://openalex.org/W7126061587","doi":"https://doi.org/10.1016/j.icte.2026.01.013"},"language":"en","primary_location":{"id":"doi:10.1016/j.icte.2026.01.013","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.icte.2026.01.013","pdf_url":null,"source":{"id":"https://openalex.org/S2898368220","display_name":"ICT Express","issn_l":"2405-9595","issn":["2405-9595"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICT Express","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1016/j.icte.2026.01.013","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100552684","display_name":"Md. Imtiaz Hossain","orcid":null},"institutions":[{"id":"https://openalex.org/I35928602","display_name":"Kyung Hee University","ror":"https://ror.org/01zqcg218","country_code":"KR","type":"education","lineage":["https://openalex.org/I35928602"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Md Imtiaz Hossain","raw_affiliation_strings":["Dept. of Computer Science and Engineering, Kyung Hee University, Global Campus, Giheung-gu, Yongin-si, 17104, Gyeonggi-do, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0003-1085-2461","affiliations":[{"raw_affiliation_string":"Dept. of Computer Science and Engineering, Kyung Hee University, Global Campus, Giheung-gu, Yongin-si, 17104, Gyeonggi-do, Republic of Korea","institution_ids":["https://openalex.org/I35928602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006975305","display_name":"Sharmen Akhter","orcid":"https://orcid.org/0000-0003-4689-7688"},"institutions":[{"id":"https://openalex.org/I35928602","display_name":"Kyung Hee University","ror":"https://ror.org/01zqcg218","country_code":"KR","type":"education","lineage":["https://openalex.org/I35928602"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sharmen Akhter","raw_affiliation_strings":["Dept. of Computer Science and Engineering, Kyung Hee University, Global Campus, Giheung-gu, Yongin-si, 17104, Gyeonggi-do, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0003-4689-7688","affiliations":[{"raw_affiliation_string":"Dept. of Computer Science and Engineering, Kyung Hee University, Global Campus, Giheung-gu, Yongin-si, 17104, Gyeonggi-do, Republic of Korea","institution_ids":["https://openalex.org/I35928602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025652295","display_name":"Sungjun Yang","orcid":"https://orcid.org/0000-0002-7821-6237"},"institutions":[{"id":"https://openalex.org/I4210148737","display_name":"CHA University","ror":"https://ror.org/04yka3j04","country_code":"KR","type":"education","lineage":["https://openalex.org/I4210148737","https://openalex.org/I4405258985"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sungjun Yang","raw_affiliation_strings":["SIGONGtech, 225-20, Pangyoyeok-ro, Bundang-gu, Seongnam-si, 13494, Gyeonggi-do, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SIGONGtech, 225-20, Pangyoyeok-ro, Bundang-gu, Seongnam-si, 13494, Gyeonggi-do, Republic of Korea","institution_ids":["https://openalex.org/I4210148737"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124208113","display_name":"Eui-Nam Huh","orcid":null},"institutions":[{"id":"https://openalex.org/I35928602","display_name":"Kyung Hee University","ror":"https://ror.org/01zqcg218","country_code":"KR","type":"education","lineage":["https://openalex.org/I35928602"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Eui-Nam Huh","raw_affiliation_strings":["Dept. of Computer Science and Engineering, Kyung Hee University, Global Campus, Giheung-gu, Yongin-si, 17104, Gyeonggi-do, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science and Engineering, Kyung Hee University, Global Campus, Giheung-gu, Yongin-si, 17104, Gyeonggi-do, Republic of Korea","institution_ids":["https://openalex.org/I35928602"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5124208113"],"corresponding_institution_ids":["https://openalex.org/I35928602"],"apc_list":{"value":1500,"currency":"USD","value_usd":1500},"apc_paid":{"value":1500,"currency":"USD","value_usd":1500},"fwci":9.8969,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.95317406,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"12","issue":"2","first_page":"480","last_page":"486"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.4970000088214874,"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":0.4970000088214874,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.37369999289512634,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.026100000366568565,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.8001999855041504},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.6837999820709229},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6499999761581421},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5511999726295471},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.45190000534057617},{"id":"https://openalex.org/keywords/rotation","display_name":"Rotation (mathematics)","score":0.4494999945163727},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.38420000672340393},{"id":"https://openalex.org/keywords/3d-pose-estimation","display_name":"3D pose estimation","score":0.3709000051021576},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.364300012588501}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8001999855041504},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7182000279426575},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.6837999820709229},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6499999761581421},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6198999881744385},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5511999726295471},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.45190000534057617},{"id":"https://openalex.org/C74050887","wikidata":"https://www.wikidata.org/wiki/Q848368","display_name":"Rotation (mathematics)","level":2,"score":0.4494999945163727},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44190001487731934},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40959998965263367},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.38420000672340393},{"id":"https://openalex.org/C36613465","wikidata":"https://www.wikidata.org/wiki/Q4636322","display_name":"3D pose estimation","level":3,"score":0.3709000051021576},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.364300012588501},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35740000009536743},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.3393999934196472},{"id":"https://openalex.org/C2776268601","wikidata":"https://www.wikidata.org/wiki/Q968808","display_name":"Occlusion","level":2,"score":0.32199999690055847},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C83633838","wikidata":"https://www.wikidata.org/wiki/Q1256564","display_name":"Rotation matrix","level":2,"score":0.28189998865127563},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C22100474","wikidata":"https://www.wikidata.org/wiki/Q4800952","display_name":"Articulated body pose estimation","level":4,"score":0.2639000117778778},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C67226441","wikidata":"https://www.wikidata.org/wiki/Q1665389","display_name":"Robust statistics","level":3,"score":0.25999999046325684},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2515999972820282}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1016/j.icte.2026.01.013","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.icte.2026.01.013","pdf_url":null,"source":{"id":"https://openalex.org/S2898368220","display_name":"ICT Express","issn_l":"2405-9595","issn":["2405-9595"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICT Express","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:99f180a01780429b86d6ce04c6b12e59","is_oa":true,"landing_page_url":"https://doaj.org/article/99f180a01780429b86d6ce04c6b12e59","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ICT Express, Vol 12, Iss 2, Pp 480-486 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1016/j.icte.2026.01.013","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.icte.2026.01.013","pdf_url":null,"source":{"id":"https://openalex.org/S2898368220","display_name":"ICT Express","issn_l":"2405-9595","issn":["2405-9595"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICT Express","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.4219822883605957,"id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G2497912785","display_name":null,"funder_award_id":"RS-2023-00222407","funder_id":"https://openalex.org/F4320322006","funder_display_name":"Ministry of Culture, Sports and Tourism"}],"funders":[{"id":"https://openalex.org/F4320322006","display_name":"Ministry of Culture, Sports and Tourism","ror":"https://ror.org/02fkk6k65"},{"id":"https://openalex.org/F4320323890","display_name":"Korea Creative Content Agency","ror":"https://ror.org/036vyg793"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2101032778","https://openalex.org/W2554247908","https://openalex.org/W2611932403","https://openalex.org/W2916798096","https://openalex.org/W2962896489","https://openalex.org/W2963995996","https://openalex.org/W2981660954","https://openalex.org/W3136525061","https://openalex.org/W4210773761","https://openalex.org/W4284993005","https://openalex.org/W4304083983","https://openalex.org/W4312689407","https://openalex.org/W4313025443","https://openalex.org/W4313161900","https://openalex.org/W4366966749","https://openalex.org/W4386075759","https://openalex.org/W4386076309","https://openalex.org/W4386083126","https://openalex.org/W4386083180","https://openalex.org/W4389626868","https://openalex.org/W4390872689"],"related_works":[],"abstract_inverted_index":{"Conventional":[0],"deep":[1],"learning-based":[2],"estimators":[3],"often":[4],"exhibit":[5],"limited":[6],"robustness":[7],"when":[8],"exposed":[9],"to":[10,82,89,124,176],"occluded":[11,24],"inputs,":[12],"as":[13],"their":[14],"training":[15,66,118],"distributions":[16],"are":[17],"biased":[18],"toward":[19],"fully":[20],"visible":[21],"or":[22],"minimally":[23],"human":[25],"poses.":[26],"To":[27],"address":[28],"this":[29],"generalization":[30],"gap,":[31],"a":[32,60,95],"novel":[33],"framework,":[34],"ORPESO":[35,93,132,193],"(Occlusion":[36],"Robust":[37],"Pose":[38],"Estimation":[39],"via":[40],"Synthetic":[41],"Occlusion),":[42],"is":[43,122],"introduced,":[44],"explicitly":[45],"incorporating":[46],"occlusion":[47,63,77],"diversity":[48],"into":[49],"both":[50,117],"the":[51,80,108,111,151],"learning":[52],"and":[53,75,106,113,119,168,173,189],"inference":[54],"stages.":[55],"The":[56],"proposed":[57],"method":[58],"constructs":[59],"comprehensive":[61],"synthetic":[62],"space":[64],"during":[65],"by":[67,163,166,171],"augmenting":[68],"clean":[69],"3D":[70],"pose":[71,86,127],"sequences":[72],"with":[73,160],"varied":[74],"structured":[76],"patterns,":[78],"encouraging":[79],"model":[81],"learn":[83],"multimodal":[84],"spatiotemporal":[85],"representations":[87],"resilient":[88],"partial":[90],"visibility.":[91],"Additionally,":[92],"leverages":[94],"test-time":[96],"adaptive":[97],"recalibration":[98],"mechanism":[99],"that":[100,192],"performs":[101],"prediction":[102,109],"on":[103,150,183],"rotated":[104,114],"samples":[105],"averages":[107],"of":[110,144],"original":[112],"samples.":[115],"During":[116],"testing,":[120],"rotation":[121],"applied":[123],"facilitate":[125],"accurate":[126],"recovery":[128],"under":[129],"severe":[130],"occlusions.":[131],"achieves":[133],"consistent":[134,195],"numerical":[135,196],"improvements":[136,143,197],"over":[137,198],"existing":[138,177],"transformer-based":[139],"methods,":[140,179],"achieving":[141],"average":[142],"15.01":[145],"mm":[146],"(20.6%)":[147],"in":[148],"MPJPE":[149,169],"Human3.6M":[152,188],"dataset.":[153],"On":[154],"MPI-INF-3DHP,":[155,190],"it":[156],"delivers":[157],"further":[158],"gains":[159],"PCK":[161],"improved":[162],"24.2%,":[164],"AUC":[165],"40.2%,":[167],"reduced":[170],"57.3%":[172],"27.6%":[174],"compared":[175],"state-of-the-art":[178,199],"respectively.":[180],"Extensive":[181],"evaluations":[182],"standard":[184],"3DHPE":[185],"benchmarks,":[186],"including":[187],"demonstrate":[191],"demonstrates":[194],"baselines.":[200]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-14T06:41:57.775601","created_date":"2026-01-30T00:00:00"}
