{"id":"https://openalex.org/W3157378612","doi":"https://doi.org/10.1109/icaiic51459.2021.9415244","title":"Multi-View 3D Human Pose Estimation with Self-Supervised Learning","display_name":"Multi-View 3D Human Pose Estimation with Self-Supervised Learning","publication_year":2021,"publication_date":"2021-04-13","ids":{"openalex":"https://openalex.org/W3157378612","doi":"https://doi.org/10.1109/icaiic51459.2021.9415244","mag":"3157378612"},"language":"en","primary_location":{"id":"doi:10.1109/icaiic51459.2021.9415244","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaiic51459.2021.9415244","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","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/A5014783126","display_name":"Inho Chang","orcid":"https://orcid.org/0000-0002-9435-2052"},"institutions":[{"id":"https://openalex.org/I4210131650","display_name":"Korea Electronics Technology Institute","ror":"https://ror.org/039k6f508","country_code":"KR","type":"facility","lineage":["https://openalex.org/I2801339556","https://openalex.org/I4210089395","https://openalex.org/I4210131650"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Inho Chang","raw_affiliation_strings":["Korea Electronics Technology Institute (KETI)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea Electronics Technology Institute (KETI)","institution_ids":["https://openalex.org/I4210131650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083111306","display_name":"Min-Gyu Park","orcid":"https://orcid.org/0000-0003-1752-150X"},"institutions":[{"id":"https://openalex.org/I4210131650","display_name":"Korea Electronics Technology Institute","ror":"https://ror.org/039k6f508","country_code":"KR","type":"facility","lineage":["https://openalex.org/I2801339556","https://openalex.org/I4210089395","https://openalex.org/I4210131650"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Min-Gyu Park","raw_affiliation_strings":["Korea Electronics Technology Institute (KETI)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea Electronics Technology Institute (KETI)","institution_ids":["https://openalex.org/I4210131650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100720873","display_name":"Jae-Woo Kim","orcid":"https://orcid.org/0000-0002-2622-4219"},"institutions":[{"id":"https://openalex.org/I4210131650","display_name":"Korea Electronics Technology Institute","ror":"https://ror.org/039k6f508","country_code":"KR","type":"facility","lineage":["https://openalex.org/I2801339556","https://openalex.org/I4210089395","https://openalex.org/I4210131650"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jaewoo Kim","raw_affiliation_strings":["Korea Electronics Technology Institute (KETI)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea Electronics Technology Institute (KETI)","institution_ids":["https://openalex.org/I4210131650"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103246577","display_name":"Ju Hong Yoon","orcid":"https://orcid.org/0000-0003-2945-8376"},"institutions":[{"id":"https://openalex.org/I4210131650","display_name":"Korea Electronics Technology Institute","ror":"https://ror.org/039k6f508","country_code":"KR","type":"facility","lineage":["https://openalex.org/I2801339556","https://openalex.org/I4210089395","https://openalex.org/I4210131650"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Ju Hong Yoon","raw_affiliation_strings":["Korea Electronics Technology Institute (KETI)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea Electronics Technology Institute (KETI)","institution_ids":["https://openalex.org/I4210131650"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210131650"],"apc_list":null,"apc_paid":null,"fwci":0.5729,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.74928138,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"255","last_page":"257"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9998999834060669,"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":0.9998999834060669,"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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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":0.9945999979972839,"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/pose","display_name":"Pose","score":0.8743290901184082},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.8123478293418884},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7929601669311523},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7786504626274109},{"id":"https://openalex.org/keywords/3d-pose-estimation","display_name":"3D pose estimation","score":0.6474802494049072},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6437886953353882},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5507543683052063},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4677068591117859},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.45894044637680054},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.43870511651039124},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43455666303634644},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35778945684432983},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06791934370994568}],"concepts":[{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.8743290901184082},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.8123478293418884},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7929601669311523},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7786504626274109},{"id":"https://openalex.org/C36613465","wikidata":"https://www.wikidata.org/wiki/Q4636322","display_name":"3D pose estimation","level":3,"score":0.6474802494049072},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6437886953353882},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5507543683052063},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4677068591117859},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.45894044637680054},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.43870511651039124},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43455666303634644},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35778945684432983},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06791934370994568},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icaiic51459.2021.9415244","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaiic51459.2021.9415244","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2030989822","https://openalex.org/W2036545421","https://openalex.org/W2052065843","https://openalex.org/W2090110089","https://openalex.org/W2101032778","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2293220651","https://openalex.org/W2342006632","https://openalex.org/W2345033116","https://openalex.org/W2554247908","https://openalex.org/W2557698284","https://openalex.org/W2583585015","https://openalex.org/W2585185777","https://openalex.org/W2605947573","https://openalex.org/W2612706635","https://openalex.org/W2797184202","https://openalex.org/W2962835968","https://openalex.org/W2963379341","https://openalex.org/W2964179555","https://openalex.org/W2968940310","https://openalex.org/W6637373629","https://openalex.org/W6684191040","https://openalex.org/W6687483927","https://openalex.org/W6729802586","https://openalex.org/W6754472501"],"related_works":["https://openalex.org/W4253893311","https://openalex.org/W3089306886","https://openalex.org/W2113785214","https://openalex.org/W2798721181","https://openalex.org/W3201205132","https://openalex.org/W4387967917","https://openalex.org/W4312694060","https://openalex.org/W4386075737","https://openalex.org/W4382141741","https://openalex.org/W2951583186"],"abstract_inverted_index":{"Modern":[0],"3D":[1,23,33,38,53],"human":[2,34,54],"pose":[3,24,35,55],"estimation":[4,36],"builds":[5],"on":[6,57],"a":[7,31],"deep":[8],"learning":[9],"network,":[10],"requiring":[11],"expensive":[12],"amounts":[13],"of":[14,20,62],"training":[15],"data":[16],"that":[17],"contain":[18],"pairs":[19],"2D":[21],"and":[22,45],"annotations.":[25,39],"In":[26],"this":[27],"paper,":[28],"we":[29,41],"propose":[30],"self-supervised":[32],"without":[37],"Instead,":[40],"exploit":[42],"multi-view":[43],"images":[44],"camera":[46],"parameters":[47],"to":[48],"make":[49],"the":[50,63],"network":[51],"learn":[52],"based":[56],"geometric":[58],"consistency.":[59],"The":[60],"merit":[61],"proposed":[64],"method":[65],"is":[66],"validated":[67],"via":[68],"experiments.":[69]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}