{"id":"https://openalex.org/W4285156324","doi":"https://doi.org/10.1109/access.2022.3177623","title":"Silhouette-Based 3D Human Pose Estimation Using a Single Wrist-Mounted 360\u00b0 Camera","display_name":"Silhouette-Based 3D Human Pose Estimation Using a Single Wrist-Mounted 360\u00b0 Camera","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4285156324","doi":"https://doi.org/10.1109/access.2022.3177623"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3177623","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3177623","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09780403.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09780403.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066297088","display_name":"Ryosuke Hori","orcid":"https://orcid.org/0000-0001-5553-2352"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ryosuke Hori","raw_affiliation_strings":["Graduate School of Science and Technology, Keio University, Yokohama, Kanagawa, Japan","Graduate School of Science and Technology, Keio University, Kanagawa 223-8522, Japan"],"raw_orcid":"https://orcid.org/0000-0001-5553-2352","affiliations":[{"raw_affiliation_string":"Graduate School of Science and Technology, Keio University, Yokohama, Kanagawa, Japan","institution_ids":["https://openalex.org/I203951103"]},{"raw_affiliation_string":"Graduate School of Science and Technology, Keio University, Kanagawa 223-8522, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020411666","display_name":"Ryo Hachiuma","orcid":"https://orcid.org/0000-0001-8274-3710"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ryo Hachiuma","raw_affiliation_strings":["Graduate School of Science and Technology, Keio University, Yokohama, Kanagawa, Japan","Graduate School of Science and Technology, Keio University, Kanagawa 223-8522, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Technology, Keio University, Yokohama, Kanagawa, Japan","institution_ids":["https://openalex.org/I203951103"]},{"raw_affiliation_string":"Graduate School of Science and Technology, Keio University, Kanagawa 223-8522, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029572039","display_name":"Mariko Isogawa","orcid":"https://orcid.org/0000-0001-9560-0276"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Mariko Isogawa","raw_affiliation_strings":["Computer and Data Science Laboratories, Nippon Telegraph and Telephone Corporation, Yokosuka, Kanagawa, Japan","Computer &#x0026"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer and Data Science Laboratories, Nippon Telegraph and Telephone Corporation, Yokosuka, Kanagawa, Japan","institution_ids":["https://openalex.org/I2251713219"]},{"raw_affiliation_string":"Computer &#x0026","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056398983","display_name":"Dan Mikami","orcid":"https://orcid.org/0000-0002-6738-4761"},"institutions":[{"id":"https://openalex.org/I116465919","display_name":"Kogakuin University","ror":"https://ror.org/01wc2tq75","country_code":"JP","type":"education","lineage":["https://openalex.org/I116465919"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Dan Mikami","raw_affiliation_strings":["Faculty of Informatics, Kogakuin University, Tokyo, Japan","Faculty of Informatics, Kogakuin University, Tokyo 163-8677, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Informatics, Kogakuin University, Tokyo, Japan","institution_ids":["https://openalex.org/I116465919"]},{"raw_affiliation_string":"Faculty of Informatics, Kogakuin University, Tokyo 163-8677, Japan","institution_ids":["https://openalex.org/I116465919"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005819073","display_name":"Hideo Sait\u00f4","orcid":"https://orcid.org/0000-0002-2421-9862"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hideo Saito","raw_affiliation_strings":["Graduate School of Science and Technology, Keio University, Yokohama, Kanagawa, Japan","Graduate School of Science and Technology, Keio University, Kanagawa 223-8522, Japan"],"raw_orcid":"https://orcid.org/0000-0002-2421-9862","affiliations":[{"raw_affiliation_string":"Graduate School of Science and Technology, Keio University, Yokohama, Kanagawa, Japan","institution_ids":["https://openalex.org/I203951103"]},{"raw_affiliation_string":"Graduate School of Science and Technology, Keio University, Kanagawa 223-8522, Japan","institution_ids":["https://openalex.org/I203951103"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.5077,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.63351377,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":"10","issue":null,"first_page":"54957","last_page":"54968"},"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9966999888420105,"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":0.9916999936103821,"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/silhouette","display_name":"Silhouette","score":0.8949980139732361},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8112169504165649},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8058653473854065},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7549580335617065},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.7092132568359375},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6195683479309082},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5330459475517273},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4447597861289978},{"id":"https://openalex.org/keywords/motion-capture","display_name":"Motion capture","score":0.4236985743045807},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.25937673449516296},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08368706703186035}],"concepts":[{"id":"https://openalex.org/C58103923","wikidata":"https://www.wikidata.org/wiki/Q2286025","display_name":"Silhouette","level":2,"score":0.8949980139732361},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8112169504165649},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8058653473854065},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7549580335617065},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.7092132568359375},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6195683479309082},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5330459475517273},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4447597861289978},{"id":"https://openalex.org/C48007421","wikidata":"https://www.wikidata.org/wiki/Q676252","display_name":"Motion capture","level":3,"score":0.4236985743045807},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.25937673449516296},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08368706703186035},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3177623","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3177623","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09780403.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:57a8328c2dc44c81a3ec2739238ce6ad","is_oa":false,"landing_page_url":"https://doaj.org/article/57a8328c2dc44c81a3ec2739238ce6ad","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 54957-54968 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3177623","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3177623","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09780403.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7880319378","display_name":"\u5206\u6563\u578b\u533f\u540d\u5316\u51e6\u7406\u306b\u3088\u308b\u30d7\u30e9\u30a4\u30d0\u30b7\u30fc\u30d7\u30ea\u30b6\u30fc\u30d6\u30c9AI\u57fa\u76e4\u69cb\u7bc9","funder_award_id":"JPMJMI19B2","funder_id":"https://openalex.org/F4320334789","funder_display_name":"Japan Science and Technology Agency"}],"funders":[{"id":"https://openalex.org/F4320334789","display_name":"Japan Science and Technology Agency","ror":"https://ror.org/00097mb19"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4285156324.pdf"},"referenced_works_count":54,"referenced_works":["https://openalex.org/W752852524","https://openalex.org/W1509064937","https://openalex.org/W1906662973","https://openalex.org/W1967554269","https://openalex.org/W2108598243","https://openalex.org/W2149276562","https://openalex.org/W2150002150","https://openalex.org/W2158782408","https://openalex.org/W2160398734","https://openalex.org/W2194775991","https://openalex.org/W2507296351","https://openalex.org/W2515603221","https://openalex.org/W2525184802","https://openalex.org/W2554247908","https://openalex.org/W2576289912","https://openalex.org/W2583585015","https://openalex.org/W2611932403","https://openalex.org/W2612706635","https://openalex.org/W2740268464","https://openalex.org/W2797184202","https://openalex.org/W2890415768","https://openalex.org/W2895641024","https://openalex.org/W2898073175","https://openalex.org/W2962730651","https://openalex.org/W2963073614","https://openalex.org/W2963598138","https://openalex.org/W2963652194","https://openalex.org/W2963772981","https://openalex.org/W2963782415","https://openalex.org/W2963791050","https://openalex.org/W2963995996","https://openalex.org/W2964179555","https://openalex.org/W2975420824","https://openalex.org/W2981801509","https://openalex.org/W2990837443","https://openalex.org/W3000322757","https://openalex.org/W3009150298","https://openalex.org/W3025441953","https://openalex.org/W3034181189","https://openalex.org/W3035367723","https://openalex.org/W3092189745","https://openalex.org/W3093797059","https://openalex.org/W3138516171","https://openalex.org/W3162727516","https://openalex.org/W3168271366","https://openalex.org/W3176249739","https://openalex.org/W3193978370","https://openalex.org/W3204860044","https://openalex.org/W4205409207","https://openalex.org/W4214518393","https://openalex.org/W4214654385","https://openalex.org/W6631190155","https://openalex.org/W6683633756","https://openalex.org/W6803780995"],"related_works":["https://openalex.org/W1622964048","https://openalex.org/W30315714","https://openalex.org/W1906975550","https://openalex.org/W1965274140","https://openalex.org/W779885325","https://openalex.org/W2150972844","https://openalex.org/W2393615320","https://openalex.org/W3110435694","https://openalex.org/W2001760863","https://openalex.org/W2067528545"],"abstract_inverted_index":{"In":[0],"this":[1,53,109,118,154,164],"paper,":[2],"we":[3,103,111,120,156],"propose":[4,89,157],"a":[5,13,23,29,61,65,72,83,90,133,138],"framework":[6],"for":[7,35,44,92,144,150,163],"3D":[8,24,99],"human":[9,25,62,76,100],"pose":[10,26,63,181],"estimation":[11,182],"using":[12],"single":[14,66],"360°":[15,95],"camera":[16,67,96],"mounted":[17],"on":[18],"the":[19,57,75,105,176],"user’s":[20],"wrist.":[21],"Perceiving":[22],"with":[27,171],"such":[28],"simple":[30],"setup":[31],"has":[32,51],"remarkable":[33],"potential":[34],"various":[36],"applications":[37],"(e.g.,":[38],"daily-living":[39],"activity":[40],"monitoring,":[41],"motion":[42],"analysis":[43],"sports":[45],"training).":[46],"However,":[47],"no":[48],"existing":[49,114],"method":[50,91,170],"tackled":[52],"task":[54],"due":[55],"to":[56,107,124],"difficulty":[58],"of":[59,74,82,85,137,178],"estimating":[60],"from":[64],"image":[68,148],"in":[69],"which":[70],"only":[71],"part":[73],"body":[77],"is":[78],"captured,":[79],"and":[80,146],"because":[81],"lack":[84],"training":[86,145],"data.":[87],"We":[88],"translating":[93],"wrist-mounted":[94],"images":[97],"into":[98],"poses.":[101],"Since":[102],"are":[104],"first":[106],"try":[108],"task,":[110],"cannot":[112],"use":[113,121],"datasets.":[115],"To":[116,152],"address":[117],"issue,":[119],"synthetic":[122,142,159],"data":[123,143,149,160],"build":[125],"our":[126,169,179],"own":[127],"dataset.":[128],"This":[129],"solution,":[130],"however,":[131],"creates":[132],"different":[134],"problem,":[135,155],"that":[136],"domain":[139],"gap":[140],"between":[141],"real":[147],"inference.":[151],"resolve":[153],"silhouette-based":[158,180],"generation":[161],"created":[162],"task.":[165],"Extensive":[166],"experiments":[167],"comparing":[168],"several":[172],"baseline":[173],"methods":[174],"demonstrated":[175],"effectiveness":[177],"approach.":[183]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}