{"id":"https://openalex.org/W4206399361","doi":"https://doi.org/10.1109/lsp.2021.3134136","title":"Pseudo View Representation Learning for Monocular RGB-D Human Pose and Shape Estimation","display_name":"Pseudo View Representation Learning for Monocular RGB-D Human Pose and Shape Estimation","publication_year":2021,"publication_date":"2021-12-09","ids":{"openalex":"https://openalex.org/W4206399361","doi":"https://doi.org/10.1109/lsp.2021.3134136"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2021.3134136","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2021.3134136","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"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 Signal Processing Letters","raw_type":"journal-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/A5006838035","display_name":"Armando Zhu","orcid":"https://orcid.org/0000-0003-2482-8104"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Armando Zhu","raw_affiliation_strings":["Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-2482-8104","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010175792","display_name":"Jiefeng Li","orcid":"https://orcid.org/0000-0003-1932-8914"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiefeng Li","raw_affiliation_strings":["Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-1932-8914","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010726528","display_name":"Cewu Lu","orcid":"https://orcid.org/0000-0002-4023-9257"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cewu Lu","raw_affiliation_strings":["MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University (SJTU), Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-4023-9257","affiliations":[{"raw_affiliation_string":"MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University (SJTU), Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6494,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.8624377,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"29","issue":null,"first_page":"712","last_page":"716"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9944000244140625,"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":0.9933000206947327,"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/rgb-color-model","display_name":"RGB color model","score":0.8949159383773804},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7915441393852234},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6982836723327637},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.6604365110397339},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6486532688140869},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.594059407711029},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5317893028259277},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.5112340450286865},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4655718505382538},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45765411853790283},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4183160662651062},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18776831030845642}],"concepts":[{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.8949159383773804},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7915441393852234},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6982836723327637},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.6604365110397339},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6486532688140869},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.594059407711029},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5317893028259277},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.5112340450286865},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4655718505382538},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45765411853790283},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4183160662651062},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18776831030845642},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2021.3134136","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2021.3134136","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"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 Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4130140615","display_name":null,"funder_award_id":"61772332","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1967554269","https://openalex.org/W2052893937","https://openalex.org/W2080873731","https://openalex.org/W2101032778","https://openalex.org/W2103015390","https://openalex.org/W2135533529","https://openalex.org/W2221659194","https://openalex.org/W2483862638","https://openalex.org/W2573098616","https://openalex.org/W2596210417","https://openalex.org/W2797184202","https://openalex.org/W2797515701","https://openalex.org/W2922375402","https://openalex.org/W2962962024","https://openalex.org/W2963926543","https://openalex.org/W2963995996","https://openalex.org/W2971467054","https://openalex.org/W2978956737","https://openalex.org/W2981637078","https://openalex.org/W2981827368","https://openalex.org/W2981978060","https://openalex.org/W2982275673","https://openalex.org/W2987588385","https://openalex.org/W2990673575","https://openalex.org/W3004162361","https://openalex.org/W3009246422","https://openalex.org/W3035291735","https://openalex.org/W3035501466","https://openalex.org/W3035549967","https://openalex.org/W3101022589","https://openalex.org/W6753506872"],"related_works":["https://openalex.org/W3162204513","https://openalex.org/W2123263858","https://openalex.org/W3127959533","https://openalex.org/W2371138613","https://openalex.org/W2048963458","https://openalex.org/W43109613","https://openalex.org/W2359952343","https://openalex.org/W2239445980","https://openalex.org/W4307623796","https://openalex.org/W4394784820"],"abstract_inverted_index":{"This":[0],"work":[1],"studies":[2],"the":[3,18,27,31,35,39,58,63,98,109,118,128,136,150],"problem":[4],"of":[5,30,38,130],"estimating":[6],"human":[7,24],"pose":[8],"and":[9,66,113,141,146],"shape":[10],"from":[11],"monocular":[12],"RGB-D":[13,19,32,41,64],"images.":[14],"Depth":[15],"information":[16],"in":[17,62],"input":[20,65],"allows":[21],"accurate":[22],"3D":[23,60,99],"reconstruction.":[25],"However,":[26],"limited":[28],"sizes":[29],"datasets":[33,70],"restrict":[34],"generalization":[36,120],"ability":[37],"existing":[40],"based":[42],"methods.":[43],"In":[44],"this":[45],"letter,":[46],"we":[47],"propose":[48],"a":[49,79],"novel":[50,80],"architecture,":[51],"View":[52],"Render":[53],"Net":[54],"(VRNet),":[55],"that":[56,104],"exploits":[57],"underlying":[59],"structure":[61,100],"utilizes":[67],"additional":[68],"RGB":[69],"for":[71],"training.":[72],"VRNet":[73,105,138],"synthesizes":[74],"pseudo":[75],"multi-view":[76,86],"representations":[77],"via":[78],"feature":[81,87],"render":[82],"approach.":[83],"The":[84],"synthetic":[85],"maps":[88],"are":[89,124],"aggregated":[90],"with":[91],"Multi-stage":[92],"Multi-view":[93],"Fusion":[94],"(MMF)":[95],"to":[96,126],"impose":[97],"constraints.":[101],"We":[102],"show":[103],"can":[106],"naturally":[107],"adopt":[108],"mixed":[110],"RGB/RGB-D":[111],"training":[112],"inference":[114],"technique,":[115],"which":[116],"improves":[117,139],"model":[119],"ability.":[121],"Comprehensive":[122],"experiments":[123],"conducted":[125],"study":[127],"effectiveness":[129],"VRNet.":[131],"As":[132],"an":[133],"illustrative":[134],"example,":[135],"proposed":[137],"MPJPE":[140],"PA-MPJPE":[142],"by":[143],"15.2":[144],"mm":[145,148],"4.9":[147],"on":[149],"Human3.6M":[151],"dataset.":[152]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
