{"id":"https://openalex.org/W4410342384","doi":"https://doi.org/10.1109/icnc64010.2025.10993617","title":"Generative AI for 3D Human Pose Completion Under RFID Sensing Constraints","display_name":"Generative AI for 3D Human Pose Completion Under RFID Sensing Constraints","publication_year":2025,"publication_date":"2025-02-17","ids":{"openalex":"https://openalex.org/W4410342384","doi":"https://doi.org/10.1109/icnc64010.2025.10993617"},"language":"en","primary_location":{"id":"doi:10.1109/icnc64010.2025.10993617","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnc64010.2025.10993617","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Computing, Networking and Communications (ICNC)","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/A5100438235","display_name":"Ziqi Wang","orcid":"https://orcid.org/0000-0002-0232-125X"},"institutions":[{"id":"https://openalex.org/I82497590","display_name":"Auburn University","ror":"https://ror.org/02v80fc35","country_code":"US","type":"education","lineage":["https://openalex.org/I82497590"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ziqi Wang","raw_affiliation_strings":["Auburn University,Auburn,AL,USA,36849-5201"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Auburn University,Auburn,AL,USA,36849-5201","institution_ids":["https://openalex.org/I82497590"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080122431","display_name":"Shiwen Mao","orcid":"https://orcid.org/0000-0002-7052-0007"},"institutions":[{"id":"https://openalex.org/I82497590","display_name":"Auburn University","ror":"https://ror.org/02v80fc35","country_code":"US","type":"education","lineage":["https://openalex.org/I82497590"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shiwen Mao","raw_affiliation_strings":["Auburn University,Auburn,AL,USA,36849-5201"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Auburn University,Auburn,AL,USA,36849-5201","institution_ids":["https://openalex.org/I82497590"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I82497590"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"485","last_page":"490"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9703999757766724,"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"}},"topics":[{"id":"https://openalex.org/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9703999757766724,"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/T13382","display_name":"Robotics and Automated Systems","score":0.9508000016212463,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10789","display_name":"Interactive and Immersive Displays","score":0.9472000002861023,"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/computer-science","display_name":"Computer science","score":0.731492817401886},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.689717173576355},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5920273065567017},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5032002329826355},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4547935128211975},{"id":"https://openalex.org/keywords/completion","display_name":"Completion (oil and gas wells)","score":0.42060402035713196},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3399183452129364},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33307939767837524},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13024231791496277}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.731492817401886},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.689717173576355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5920273065567017},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5032002329826355},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4547935128211975},{"id":"https://openalex.org/C2779538338","wikidata":"https://www.wikidata.org/wiki/Q2990590","display_name":"Completion (oil and gas wells)","level":2,"score":0.42060402035713196},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3399183452129364},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33307939767837524},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13024231791496277},{"id":"https://openalex.org/C78762247","wikidata":"https://www.wikidata.org/wiki/Q1273174","display_name":"Petroleum engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icnc64010.2025.10993617","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnc64010.2025.10993617","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Computing, Networking and Communications (ICNC)","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":17,"referenced_works":["https://openalex.org/W2827033964","https://openalex.org/W2968303571","https://openalex.org/W2975941018","https://openalex.org/W2978956737","https://openalex.org/W2990165697","https://openalex.org/W3021587713","https://openalex.org/W3096801167","https://openalex.org/W3152978100","https://openalex.org/W3175199633","https://openalex.org/W4205190037","https://openalex.org/W4225103618","https://openalex.org/W4361801761","https://openalex.org/W4390872365","https://openalex.org/W4391305822","https://openalex.org/W4403446186","https://openalex.org/W6779823529","https://openalex.org/W6846964981"],"related_works":["https://openalex.org/W4365211920","https://openalex.org/W3014948380","https://openalex.org/W4391584540","https://openalex.org/W4380551139","https://openalex.org/W4317695495","https://openalex.org/W4395044357","https://openalex.org/W4287117424","https://openalex.org/W4387506531","https://openalex.org/W2087346071","https://openalex.org/W2967848559"],"abstract_inverted_index":{"Accurate":[0],"3D":[1,47,98,135],"human":[2,136],"pose":[3,61,137],"estimation":[4,138],"(HPE)":[5,139],"from":[6,51,69,109],"wireless":[7,14,132],"signals":[8],"is":[9,120],"highly":[10],"challenging":[11],"due":[12],"to":[13,72,95,124],"sensing":[15,168,181],"constraints.":[16],"A":[17],"functional":[18],"platform":[19],"typically":[20],"requires":[21],"a":[22,81,89,115],"comprehensive":[23],"setup":[24],"of":[25,39,148],"transceivers":[26],"and":[27,54,158,174],"antennas,":[28],"such":[29,170],"as":[30,63,118,171],"WiFi":[31],"devices,":[32],"FMCW":[33],"radars,":[34],"or":[35,179],"RFID":[36,65,167],"tags,":[37],"all":[38],"which":[40],"come":[41],"with":[42,88],"their":[43],"own":[44],"limitations.":[45],"RFID-based":[46],"HPE":[48],"often":[49],"suffers":[50],"tag":[52],"interference":[53],"sparse":[55],"readings,":[56],"resulting":[57],"in":[58,150,177],"incomplete":[59],"skeletal":[60,129],"estimations,":[62],"current":[64],"systems":[66],"capture":[67],"information":[68],"only":[70],"up":[71],"12":[73],"joints.":[74],"To":[75],"address":[76],"this":[77],"challenge,":[78],"we":[79],"propose":[80],"novel":[82],"latent":[83],"diffusion":[84],"transformer":[85],"(LDT)":[86],"framework":[87,162],"cross-attention":[90],"conditioning":[91],"method,":[92],"termed":[93],"PoseCompLDT,":[94],"accurately":[96],"complete":[97],"poses":[99],"by":[100,155],"generating":[101],"the":[102,121,146],"missing":[103],"joints,":[104],"enabling":[105],"full":[106],"25-joint":[107],"configurations":[108],"partial":[110],"12-joint":[111],"inputs.":[112],"This":[113],"marks":[114],"significant":[116],"advance,":[117],"it":[119],"first":[122],"approach":[123],"achieve":[125],"over":[126],"20":[127],"distinct":[128],"joints":[130],"for":[131],"sensing-based":[133],"continuous":[134],"using":[140],"generative":[141],"AI.":[142],"Extensive":[143],"experiments":[144],"validate":[145],"effectiveness":[147],"PoseCompLDT":[149],"preserving":[151],"motion":[152],"fidelity,":[153],"supported":[154],"rigorous":[156],"qualitative":[157],"quantitative":[159],"studies.":[160],"The":[161],"offers":[163],"scalable":[164],"solutions":[165],"beyond":[166],"applications,":[169],"pedestrian":[172],"tracking":[173],"health":[175],"monitoring":[176],"occluded":[178],"constrained":[180],"environments.":[182]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
