{"id":"https://openalex.org/W4416250045","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228777","title":"Dilated Point Spatio-Temporal Mesh Transformer for mmWave Radar-Based Human Mesh Reconstruction","display_name":"Dilated Point Spatio-Temporal Mesh Transformer for mmWave Radar-Based Human Mesh Reconstruction","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416250045","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228777"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228777","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228777","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5100746873","display_name":"Lin Chen","orcid":"https://orcid.org/0009-0002-0639-2191"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Chen","raw_affiliation_strings":["Sun Yat-Sen University,School of Computer Science and Engineering,Guangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University,School of Computer Science and Engineering,Guangzhou,China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100696243","display_name":"Guoli Wang","orcid":"https://orcid.org/0000-0002-0318-8905"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoli Wang","raw_affiliation_strings":["Sun Yat-Sen University,School of Software Engineering,Zhuhai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University,School of Software Engineering,Zhuhai,China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I157773358"],"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":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.6586999893188477,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.6586999893188477,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.08309999853372574,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.031300000846385956,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.5562999844551086},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5497000217437744},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.512499988079071},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.35089999437332153},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.31540000438690186},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.30300000309944153},{"id":"https://openalex.org/keywords/clutter","display_name":"Clutter","score":0.2809000015258789}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6929000020027161},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.5562999844551086},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5544999837875366},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5497000217437744},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5321000218391418},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.512499988079071},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.35089999437332153},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.31540000438690186},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.30300000309944153},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.2809000015258789},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.2808000147342682},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2757999897003174},{"id":"https://openalex.org/C174440990","wikidata":"https://www.wikidata.org/wiki/Q681349","display_name":"Point-to-point","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C134406370","wikidata":"https://www.wikidata.org/wiki/Q832005","display_name":"Radar engineering details","level":4,"score":0.27250000834465027},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.2603999972343445},{"id":"https://openalex.org/C150140777","wikidata":"https://www.wikidata.org/wiki/Q960648","display_name":"Point of interest","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228777","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228777","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"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":32,"referenced_works":["https://openalex.org/W1967554269","https://openalex.org/W2963907666","https://openalex.org/W2963995996","https://openalex.org/W2978956737","https://openalex.org/W3035551320","https://openalex.org/W3109877674","https://openalex.org/W3168718178","https://openalex.org/W3175199633","https://openalex.org/W3175987492","https://openalex.org/W3196703792","https://openalex.org/W3201052417","https://openalex.org/W3204177023","https://openalex.org/W3212616108","https://openalex.org/W4214684804","https://openalex.org/W4214755140","https://openalex.org/W4288049158","https://openalex.org/W4295682580","https://openalex.org/W4312518484","https://openalex.org/W4312595848","https://openalex.org/W4319663728","https://openalex.org/W4379983224","https://openalex.org/W4383503536","https://openalex.org/W4388819712","https://openalex.org/W4389160168","https://openalex.org/W4390075115","https://openalex.org/W4392902908","https://openalex.org/W4394625766","https://openalex.org/W4402351667","https://openalex.org/W4403210269","https://openalex.org/W4403842725","https://openalex.org/W4403908379","https://openalex.org/W4404056729"],"related_works":[],"abstract_inverted_index":{"Millimeter":[0],"wave":[1],"(mmWave)":[2],"radar-based":[3],"human":[4,38,59,70,140],"mesh":[5,60,71,141],"reconstruction":[6,72],"has":[7,80],"attracted":[8],"attention":[9,109],"due":[10],"to":[11,14,24,94,158,171],"its":[12],"robustness":[13,46],"environments":[15],"and":[16,26,34,47,108,121,125,135,148,168],"privacy.":[17],"However,":[18],"radar":[19,100,165],"point":[20,101,166],"clouds":[21,167],"are":[22,156],"susceptible":[23],"noise":[25,153,173],"have":[27],"limited":[28],"spatial":[29],"resolution,":[30],"thus":[31],"cannot":[32],"accurately":[33],"completely":[35],"represent":[36],"the":[37,43,53,57,64,86,105,111,144,149,160,172,175,180,183],"body":[39],"shape.":[40],"In":[41,179],"addition,":[42],"lack":[44],"of":[45,114,164],"fine-grained":[48,136],"local":[49,96,115,127],"detail":[50],"features":[51],"in":[52,99,174],"existing":[54],"methods":[55],"complicates":[56],"accurate":[58],"reconstruction.":[61,142],"Focus":[62],"on":[63],"above":[65],"problems,":[66],"a":[67,122],"novel":[68],"Transformer-based":[69],"method":[73,185],"Dilated":[74,89],"Point":[75,90],"spatio-temporal":[76,91,116,146,162],"Mesh":[77],"Transformer":[78],"(DPMesh)":[79],"been":[81],"proposed.":[82],"The":[83],"DPMesh":[84],"uses":[85],"proposed":[87,184],"Multi-Scale":[88],"Attention":[92],"(MSDPA)":[93],"learn":[95,159],"spatiotemporal":[97],"dependencies":[98,163],"clouds.":[102],"By":[103],"combining":[104],"dilated":[106],"convolution":[107],"mechanism,":[110],"multiple":[112],"ranges":[113],"regions":[117],"can":[118,130,186],"be":[119,131],"sensed,":[120],"wider":[123],"range":[124],"multi-scale":[126],"context":[128],"information":[129],"captured,":[132],"providing":[133],"robust":[134],"perceptual":[137],"cues":[138],"for":[139],"Moreover,":[143],"global":[145,161],"self-attention":[147],"added":[150],"clipped":[151],"Gaussian":[152],"during":[154],"training":[155,176],"used":[157],"reduce":[169],"overfitting":[170],"set,":[177],"respectively.":[178],"mmBody":[181],"dataset,":[182],"achieve":[187],"state-of-the-art":[188],"results.":[189]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-14T00:00:00"}
