{"id":"https://openalex.org/W4410359170","doi":"https://doi.org/10.1109/tim.2025.3569914","title":"Knowledge-Embedded Transformer for 3-D Human Pose Estimation","display_name":"Knowledge-Embedded Transformer for 3-D Human Pose Estimation","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4410359170","doi":"https://doi.org/10.1109/tim.2025.3569914"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2025.3569914","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2025.3569914","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Transactions on Instrumentation and Measurement","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/A5100347302","display_name":"Shu Chen","orcid":"https://orcid.org/0000-0003-4384-0811"},"institutions":[{"id":"https://openalex.org/I4610292","display_name":"Xiangtan University","ror":"https://ror.org/00xsfaz62","country_code":"CN","type":"education","lineage":["https://openalex.org/I4610292"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shu Chen","raw_affiliation_strings":["School of Computer Science, Xiangtan University, Xiangtan, China","School of Computer Science.School of Cyberspace Security, Xiangtan University, Xiangtan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Xiangtan University, Xiangtan, China","institution_ids":["https://openalex.org/I4610292"]},{"raw_affiliation_string":"School of Computer Science.School of Cyberspace Security, Xiangtan University, Xiangtan, China","institution_ids":["https://openalex.org/I4610292"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109509667","display_name":"Ying He","orcid":null},"institutions":[{"id":"https://openalex.org/I4610292","display_name":"Xiangtan University","ror":"https://ror.org/00xsfaz62","country_code":"CN","type":"education","lineage":["https://openalex.org/I4610292"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying He","raw_affiliation_strings":["School of Computer Science, Xiangtan University, Xiangtan, China","School of Computer Science.School of Cyberspace Security, Xiangtan University, Xiangtan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Xiangtan University, Xiangtan, China","institution_ids":["https://openalex.org/I4610292"]},{"raw_affiliation_string":"School of Computer Science.School of Cyberspace Security, Xiangtan University, Xiangtan, China","institution_ids":["https://openalex.org/I4610292"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100347302"],"corresponding_institution_ids":["https://openalex.org/I4610292"],"apc_list":null,"apc_paid":null,"fwci":0.6954,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.68959722,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"74","issue":null,"first_page":"1","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9961000084877014,"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":0.9803000092506409,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5458607077598572},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4909527599811554},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.43196743726730347},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.347159206867218},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2541065812110901},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.23852264881134033},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.22272637486457825}],"concepts":[{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5458607077598572},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4909527599811554},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.43196743726730347},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.347159206867218},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2541065812110901},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.23852264881134033},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.22272637486457825}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2025.3569914","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2025.3569914","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Transactions on Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1967554269","https://openalex.org/W2080873731","https://openalex.org/W2101032778","https://openalex.org/W2103015390","https://openalex.org/W2122633688","https://openalex.org/W2135533529","https://openalex.org/W2483862638","https://openalex.org/W2797184202","https://openalex.org/W2798637590","https://openalex.org/W2895748257","https://openalex.org/W2934361577","https://openalex.org/W2949924544","https://openalex.org/W2956061722","https://openalex.org/W2963876278","https://openalex.org/W2963907666","https://openalex.org/W2963995996","https://openalex.org/W2965523038","https://openalex.org/W2975420824","https://openalex.org/W2981637078","https://openalex.org/W2990270790","https://openalex.org/W3035501466","https://openalex.org/W3035551320","https://openalex.org/W3107073427","https://openalex.org/W3107346586","https://openalex.org/W3108664802","https://openalex.org/W3109877674","https://openalex.org/W3126541466","https://openalex.org/W3128810022","https://openalex.org/W3136525061","https://openalex.org/W3145609993","https://openalex.org/W3167491448","https://openalex.org/W3173283771","https://openalex.org/W3175199633","https://openalex.org/W3204956438","https://openalex.org/W3209771269","https://openalex.org/W4205116876","https://openalex.org/W4214586188","https://openalex.org/W4214684804","https://openalex.org/W4214770715","https://openalex.org/W4221142859","https://openalex.org/W4292263294","https://openalex.org/W4292837409","https://openalex.org/W4312261745","https://openalex.org/W4312518484","https://openalex.org/W4312677021","https://openalex.org/W4366606483","https://openalex.org/W4383428317","https://openalex.org/W4385245566","https://openalex.org/W4386071471","https://openalex.org/W4386076469","https://openalex.org/W4386320389","https://openalex.org/W4390190131","https://openalex.org/W4390874306","https://openalex.org/W4393171277","https://openalex.org/W6631190155","https://openalex.org/W6767133472"],"related_works":["https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4391695420","https://openalex.org/W2711238126","https://openalex.org/W4308083507","https://openalex.org/W2162788266","https://openalex.org/W2286009621","https://openalex.org/W2900273708","https://openalex.org/W3103544346","https://openalex.org/W4282941460"],"abstract_inverted_index":{"Despite":[0],"the":[1,7,32,123,158,163,190,198],"significant":[2],"advancements":[3],"in":[4,54,197],"deep":[5,46],"learning,":[6],"task":[8],"of":[9,119],"estimating":[10],"3D":[11],"human":[12,39,108,170],"pose":[13,113],"and":[14,41,70,102,135],"shape":[15],"from":[16,66],"a":[17,45,78,92,126],"monocular":[18],"RGB":[19],"image":[20,59,103],"remains":[21],"challenging,":[22],"primarily":[23],"due":[24,185],"to":[25,62,84,95,152,179,186,192,216],"depth":[26],"ambiguity.":[27],"Regression":[28],"approaches":[29,51],"based":[30,99],"on":[31,100,205],"SMPL":[33],"model":[34],"iteratively":[35],"predict":[36,194],"joint":[37],"rotations,":[38],"shape,":[40],"camera":[42,200],"parameters":[43,98],"through":[44],"neural":[47],"network.":[48],"However,":[49],"these":[50],"encounter":[52],"difficulties":[53],"aligning":[55],"predicted":[56],"meshes":[57],"with":[58],"pixels,":[60],"attributed":[61],"cumulative":[63],"errors":[64],"arising":[65],"complex":[67],"limb":[68,86,112,120],"movements":[69],"occlusion.":[71],"To":[72,110],"address":[73],"this":[74],"challenge,":[75],"we":[76,115,147,168],"propose":[77],"Left-Right":[79],"Limb":[80],"Appearance":[81],"Consistency":[82],"module":[83],"enhance":[85],"reconstruction":[87],"precision.":[88],"Our":[89],"approach":[90,212],"utilizes":[91],"feature":[93],"pyramid":[94],"correct":[96],"model-predicted":[97],"mesh":[101],"alignment":[104],"status,":[105],"progressively":[106],"refining":[107],"meshes.":[109],"improve":[111],"estimation,":[114],"incorporate":[116],"prior":[117],"knowledge":[118],"symmetry":[121],"into":[122],"networks,":[124],"employing":[125],"cross-attention":[127],"mechanism":[128],"that":[129,142,210],"models":[130],"spatial":[131],"dependencies":[132],"between":[133],"left":[134],"right":[136],"limbs.":[137],"Unlike":[138],"conventional":[139],"regressionbased":[140],"methods":[141],"use":[143],"weak":[144],"perspective":[145,150],"projection,":[146],"employ":[148],"full":[149],"projection":[151,155],"obtain":[153],"2D":[154],"points":[156],"across":[157],"entire":[159],"image,":[160],"subsequently":[161],"revising":[162],"reprojection":[164],"loss":[165],"function.":[166],"Furthermore,":[167],"introduce":[169],"global":[171,195],"position":[172],"information":[173],"as":[174],"supervision":[175],"during":[176],"network":[177,191],"training":[178],"compensate":[180],"for":[181],"missing":[182],"positional":[183],"data":[184],"cropping.":[187],"This":[188],"enables":[189],"accurately":[193],"rotation":[196],"original":[199],"coordinate":[201],"system.":[202],"Extensive":[203],"experiments":[204],"widely":[206],"used":[207],"datasets":[208],"demonstrate":[209],"our":[211],"yields":[213],"improvements":[214],"compared":[215],"several":[217],"state-of-the-art":[218],"methods.":[219],"The":[220],"code":[221],"is":[222],"available":[223],"at":[224],"https://github.com/XTU-PR-LAB/PAMA.":[225]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
