{"id":"https://openalex.org/W3205759855","doi":"https://doi.org/10.1109/avss52988.2021.9663755","title":"Learning Temporal 3D Human Pose Estimation with Pseudo-Labels","display_name":"Learning Temporal 3D Human Pose Estimation with Pseudo-Labels","publication_year":2021,"publication_date":"2021-11-16","ids":{"openalex":"https://openalex.org/W3205759855","doi":"https://doi.org/10.1109/avss52988.2021.9663755","mag":"3205759855"},"language":"en","primary_location":{"id":"doi:10.1109/avss52988.2021.9663755","is_oa":false,"landing_page_url":"https://doi.org/10.1109/avss52988.2021.9663755","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","raw_type":"proceedings-article"},"type":"preprint","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/A5071460045","display_name":"Arij Bouazizi","orcid":null},"institutions":[{"id":"https://openalex.org/I196349391","display_name":"Universit\u00e4t Ulm","ror":"https://ror.org/032000t02","country_code":"DE","type":"education","lineage":["https://openalex.org/I196349391"]},{"id":"https://openalex.org/I1332474105","display_name":"Mercedes-Benz (Germany)","ror":"https://ror.org/055rn2a38","country_code":"DE","type":"company","lineage":["https://openalex.org/I1332474105"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Arij Bouazizi","raw_affiliation_strings":["Mercedes-Benz AG, Stuttgart, Germany","Universit\u00e4t Ulm, Ulm, Germany"],"affiliations":[{"raw_affiliation_string":"Mercedes-Benz AG, Stuttgart, Germany","institution_ids":["https://openalex.org/I1332474105"]},{"raw_affiliation_string":"Universit\u00e4t Ulm, Ulm, Germany","institution_ids":["https://openalex.org/I196349391"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113850169","display_name":"Ulrich Kre\u00dfel","orcid":null},"institutions":[{"id":"https://openalex.org/I1332474105","display_name":"Mercedes-Benz (Germany)","ror":"https://ror.org/055rn2a38","country_code":"DE","type":"company","lineage":["https://openalex.org/I1332474105"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ulrich Kressel","raw_affiliation_strings":["Mercedes-Benz AG, Stuttgart, Germany"],"affiliations":[{"raw_affiliation_string":"Mercedes-Benz AG, Stuttgart, Germany","institution_ids":["https://openalex.org/I1332474105"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027065196","display_name":"Vasileios Belagiannis","orcid":"https://orcid.org/0000-0003-0960-8453"},"institutions":[{"id":"https://openalex.org/I196349391","display_name":"Universit\u00e4t Ulm","ror":"https://ror.org/032000t02","country_code":"DE","type":"education","lineage":["https://openalex.org/I196349391"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Vasileios Belagiannis","raw_affiliation_strings":["Universit\u00e4t Ulm, Ulm, Germany"],"affiliations":[{"raw_affiliation_string":"Universit\u00e4t Ulm, Ulm, Germany","institution_ids":["https://openalex.org/I196349391"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5071460045"],"corresponding_institution_ids":["https://openalex.org/I196349391","https://openalex.org/I1332474105"],"apc_list":null,"apc_paid":null,"fwci":0.51109739,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.65497286,"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":"1","last_page":"8"},"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/T11227","display_name":"Diabetic Foot Ulcer Assessment and Management","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9897000193595886,"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":0.898760199546814},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8034350872039795},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.7367382645606995},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6945752501487732},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6796327829360962},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.6381832361221313},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6362773776054382},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6212570667266846},{"id":"https://openalex.org/keywords/3d-pose-estimation","display_name":"3D pose estimation","score":0.5261309146881104},{"id":"https://openalex.org/keywords/articulated-body-pose-estimation","display_name":"Articulated body pose estimation","score":0.46137747168540955},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4505307674407959},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4073527455329895},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38014036417007446}],"concepts":[{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.898760199546814},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8034350872039795},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.7367382645606995},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6945752501487732},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6796327829360962},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.6381832361221313},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6362773776054382},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6212570667266846},{"id":"https://openalex.org/C36613465","wikidata":"https://www.wikidata.org/wiki/Q4636322","display_name":"3D pose estimation","level":3,"score":0.5261309146881104},{"id":"https://openalex.org/C22100474","wikidata":"https://www.wikidata.org/wiki/Q4800952","display_name":"Articulated body pose estimation","level":4,"score":0.46137747168540955},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4505307674407959},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4073527455329895},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38014036417007446},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/avss52988.2021.9663755","is_oa":false,"landing_page_url":"https://doi.org/10.1109/avss52988.2021.9663755","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320307110","display_name":"Delta","ror":"https://ror.org/03g9c1e75"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W16266776","https://openalex.org/W2033819227","https://openalex.org/W2101032778","https://openalex.org/W2135826343","https://openalex.org/W2256477790","https://openalex.org/W2345308174","https://openalex.org/W2483862638","https://openalex.org/W2522527348","https://openalex.org/W2554247908","https://openalex.org/W2558486663","https://openalex.org/W2612706635","https://openalex.org/W2756050327","https://openalex.org/W2893627667","https://openalex.org/W2934361577","https://openalex.org/W2962786630","https://openalex.org/W2962896489","https://openalex.org/W2963379341","https://openalex.org/W2963441822","https://openalex.org/W2963590054","https://openalex.org/W2963995996","https://openalex.org/W2964179555","https://openalex.org/W2964221239","https://openalex.org/W2968940310","https://openalex.org/W2970285700","https://openalex.org/W2981637078","https://openalex.org/W2982101758","https://openalex.org/W2997760858","https://openalex.org/W3009881372","https://openalex.org/W3034482680","https://openalex.org/W3035072447","https://openalex.org/W3048573950","https://openalex.org/W3106165820","https://openalex.org/W3130641941","https://openalex.org/W3177949351","https://openalex.org/W3194851579","https://openalex.org/W6730522252","https://openalex.org/W6754472501","https://openalex.org/W6755322230","https://openalex.org/W6756515473","https://openalex.org/W6779669310","https://openalex.org/W6782095612","https://openalex.org/W6800308654"],"related_works":["https://openalex.org/W2113785214","https://openalex.org/W2946083937","https://openalex.org/W2798721181","https://openalex.org/W4299867837","https://openalex.org/W4386075737","https://openalex.org/W2951583186","https://openalex.org/W1974260915","https://openalex.org/W4382141741","https://openalex.org/W2088028039","https://openalex.org/W4294967731"],"abstract_inverted_index":{"We":[0],"present":[1],"a":[2,38,74,82],"simple,":[3],"yet":[4],"effective,":[5],"approach":[6],"for":[7,90],"self-supervised":[8],"3D":[9,52,66,87],"human":[10],"pose":[11,35,79,89],"estimation.":[12],"Unlike":[13],"the":[14,19,24,50,55,64,86,105],"prior":[15],"work,":[16],"we":[17,29],"explore":[18],"temporal":[20,43],"information":[21],"next":[22],"to":[23,84],"multi-view":[25,57],"self-supervision.":[26],"During":[27,69],"training,":[28],"rely":[30],"on":[31,63],"triangulating":[32],"2D":[33,77],"body":[34,67,78,88],"estimates":[36,80],"of":[37,76,92],"multiple-view":[39],"camera":[40],"system.":[41],"A":[42],"convolutional":[44],"neural":[45],"network":[46],"is":[47],"trained":[48],"with":[49],"generated":[51],"ground-truth":[53],"and":[54,107,112],"geometric":[56],"consistency":[58],"loss,":[59],"imposing":[60],"geometrical":[61],"constraints":[62],"predicted":[65],"skeleton.":[68],"inference,":[70],"our":[71,99],"model":[72],"receives":[73],"sequence":[75],"from":[81],"single-view":[83],"predict":[85],"each":[91],"them.":[93],"An":[94],"extensive":[95],"evaluation":[96],"shows":[97],"that":[98],"method":[100],"achieves":[101],"state-of-the-art":[102],"performance":[103],"in":[104],"Human3.6M":[106],"MPI-INF-3DHP":[108],"benchmarks.":[109],"Our":[110],"code":[111],"models":[113],"are":[114],"publicly":[115],"available":[116],"at":[117],"https://github.com/vru2020/TM_HPE/.":[118]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
