{"id":"https://openalex.org/W4304083983","doi":"https://doi.org/10.1145/3503161.3547773","title":"Uncertainty-Aware 3D Human Pose Estimation from Monocular Video","display_name":"Uncertainty-Aware 3D Human Pose Estimation from Monocular Video","publication_year":2022,"publication_date":"2022-10-10","ids":{"openalex":"https://openalex.org/W4304083983","doi":"https://doi.org/10.1145/3503161.3547773"},"language":"en","primary_location":{"id":"doi:10.1145/3503161.3547773","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3547773","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"article","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/A5041539748","display_name":"Jinlu Zhang","orcid":"https://orcid.org/0000-0002-8731-7099"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinlu Zhang","raw_affiliation_strings":["Wuhan University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101416291","display_name":"Yujin Chen","orcid":"https://orcid.org/0000-0003-3709-6125"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Yujin Chen","raw_affiliation_strings":["Technical University of Munich, Munich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Munich, Munich, Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074405661","display_name":"Zhigang Tu","orcid":"https://orcid.org/0000-0001-5003-2260"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhigang Tu","raw_affiliation_strings":["Wuhan University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.5927,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.8898275,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"5102","last_page":"5113"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9998000264167786,"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":0.9998000264167786,"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":0.996999979019165,"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":0.996999979019165,"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":0.7359524965286255},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7127770185470581},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5960798263549805},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5523020029067993},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.534617006778717},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.5244922041893005},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5218878388404846},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4901176393032074},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4897015392780304},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.48364531993865967},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.48104336857795715},{"id":"https://openalex.org/keywords/measurement-uncertainty","display_name":"Measurement uncertainty","score":0.4404262602329254},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.43306946754455566},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4276588559150696},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.41272294521331787},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.41059035062789917},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4074653387069702},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40438640117645264},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15924754738807678},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08555397391319275}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7359524965286255},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7127770185470581},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5960798263549805},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5523020029067993},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.534617006778717},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.5244922041893005},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5218878388404846},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4901176393032074},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4897015392780304},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.48364531993865967},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.48104336857795715},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.4404262602329254},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.43306946754455566},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4276588559150696},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.41272294521331787},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.41059035062789917},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4074653387069702},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40438640117645264},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15924754738807678},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08555397391319275},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3503161.3547773","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3547773","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G7860316737","display_name":null,"funder_award_id":"62106177","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W2101032778","https://openalex.org/W2307770531","https://openalex.org/W2554247908","https://openalex.org/W2611932403","https://openalex.org/W2612706635","https://openalex.org/W2756050327","https://openalex.org/W2797184202","https://openalex.org/W2798646183","https://openalex.org/W2916798096","https://openalex.org/W2962896489","https://openalex.org/W2963076818","https://openalex.org/W2963150697","https://openalex.org/W2963402313","https://openalex.org/W2963592930","https://openalex.org/W2963598138","https://openalex.org/W2963781481","https://openalex.org/W2964221239","https://openalex.org/W2966735886","https://openalex.org/W2972662547","https://openalex.org/W2985923700","https://openalex.org/W2987087879","https://openalex.org/W2990138404","https://openalex.org/W2998027150","https://openalex.org/W3009246422","https://openalex.org/W3025281718","https://openalex.org/W3034217102","https://openalex.org/W3034448411","https://openalex.org/W3035416506","https://openalex.org/W3037859578","https://openalex.org/W3097623574","https://openalex.org/W3100019193","https://openalex.org/W3106838237","https://openalex.org/W3106882556","https://openalex.org/W3122282950","https://openalex.org/W3126541466","https://openalex.org/W3136525061","https://openalex.org/W3169891778","https://openalex.org/W3173283771","https://openalex.org/W3173811519","https://openalex.org/W3174541392","https://openalex.org/W3178872387","https://openalex.org/W3195639294","https://openalex.org/W3203617912","https://openalex.org/W3203925315","https://openalex.org/W3205327953","https://openalex.org/W4214751560","https://openalex.org/W4225557002","https://openalex.org/W4229890965","https://openalex.org/W4312417903","https://openalex.org/W6766771472","https://openalex.org/W6768698167"],"related_works":["https://openalex.org/W1991093342","https://openalex.org/W2123263858","https://openalex.org/W3127959533","https://openalex.org/W200819717","https://openalex.org/W2078622645","https://openalex.org/W4387967917","https://openalex.org/W2170798819","https://openalex.org/W4386925306","https://openalex.org/W4307623796","https://openalex.org/W4394784820"],"abstract_inverted_index":{"Estimating":[0],"the":[1,6,14,24,32,37,52,56,66,72,76,81,85,93,97,104,110,157,165],"3D":[2,28,116],"human":[3,29,117],"pose":[4,30],"from":[5],"monocular":[7],"video":[8],"is":[9],"challenging":[10],"mainly":[11],"due":[12],"to":[13,40,50,64,74,91,113,120,140],"depth":[15,25,41,77,105],"ambiguity":[16],"and":[17,83,108,138,154,156],"inaccurate":[18,98],"2D":[19,57,99,111],"detected":[20],"keypoints.":[21,100],"To":[22],"quantify":[23],"uncertainty":[26,38,54,79,95,106,126],"of":[27,55,80,96],"via":[31],"neural":[33],"network,":[34,82],"we":[35,59,70,102,128],"imbue":[36],"modeling":[39],"prediction":[42,78],"by":[43,167],"using":[44],"evidential":[45],"deep":[46],"learning":[47],"(EDL).":[48],"Meanwhile,":[49],"calibrate":[51,109],"distribution":[53],"detection,":[58],"explore":[60],"a":[61,168],"probabilistic":[62],"representation":[63],"model":[65,92,163],"realistic":[67],"distribution.":[68],"Specifically,":[69],"exploit":[71],"EDL":[73],"measure":[75],"decompose":[84],"x-y":[86],"coordinates":[87],"into":[88],"individual":[89],"distributions":[90],"deviation":[94],"Then":[101],"optimize":[103],"parameters":[107],"deviations":[112],"obtain":[114],"accurate":[115],"poses.":[118],"Besides,":[119],"provide":[121],"effective":[122],"latent":[123],"features":[124],"for":[125],"learning,":[127],"design":[129],"an":[130],"encoder":[131],"which":[132],"combines":[133],"graph":[134],"convolutional":[135],"network":[136],"(GCN)":[137],"transformer":[139],"learn":[141],"discriminative":[142],"spatio-temporal":[143],"representations.":[144],"Extensive":[145],"experiments":[146],"are":[147],"conducted":[148],"on":[149],"three":[150],"benchmarks":[151],"(Human3.6M,":[152],"MPI-INF-3DHP,":[153],"HumanEva-I)":[155],"comprehensive":[158],"results":[159],"show":[160],"that":[161],"our":[162],"surpasses":[164],"state-of-the-arts":[166],"large":[169],"margin.":[170]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":7}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}