{"id":"https://openalex.org/W7166685998","doi":"https://doi.org/10.48550/arxiv.2606.28769","title":"Generative Learning as a Tool to Improve Perception of Emotional Body Motion Expressions","display_name":"Generative Learning as a Tool to Improve Perception of Emotional Body Motion Expressions","publication_year":2026,"publication_date":"2026-06-27","ids":{"openalex":"https://openalex.org/W7166685998","doi":"https://doi.org/10.48550/arxiv.2606.28769"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.28769","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28769","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.28769","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064341150","display_name":"Huakun Liu","orcid":"https://orcid.org/0000-0002-9130-2519"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Huakun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139643074","display_name":"Miao Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Miao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139649390","display_name":"Xin Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, Xin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046882043","display_name":"Felix Dollack","orcid":"https://orcid.org/0000-0002-0399-1079"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dollack, Felix","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119949179","display_name":"Victor Schneider","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schneider, Victor","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139701336","display_name":"Hideaki Uchiyama","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Uchiyama, Hideaki","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136477876","display_name":"Chia-huei Tseng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tseng, Chia-huei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026275950","display_name":"Yoshifumi Kitamura","orcid":"https://orcid.org/0000-0002-7047-627X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kitamura, Yoshifumi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5076689421","display_name":"Monica Perusqu\u00eda-Hern\u00e1ndez","orcid":"https://orcid.org/0000-0002-0486-1743"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Perusquia-Hernandez, Monica","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.7276999950408936,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.7276999950408936,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10709","display_name":"Social Robot Interaction and HRI","score":0.16290000081062317,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.026499999687075615,"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/generative-grammar","display_name":"Generative grammar","score":0.7896000146865845},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.6450999975204468},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.6413000226020813},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.6385999917984009},{"id":"https://openalex.org/keywords/emotional-expression","display_name":"Emotional expression","score":0.5730000138282776},{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"Emotion recognition","score":0.5435000061988831},{"id":"https://openalex.org/keywords/motion-capture","display_name":"Motion capture","score":0.5001000165939331},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4749999940395355}],"concepts":[{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.7896000146865845},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.6450999975204468},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.6413000226020813},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.6385999917984009},{"id":"https://openalex.org/C143110190","wikidata":"https://www.wikidata.org/wiki/Q5373787","display_name":"Emotional expression","level":2,"score":0.5730000138282776},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.5435000061988831},{"id":"https://openalex.org/C48007421","wikidata":"https://www.wikidata.org/wiki/Q676252","display_name":"Motion capture","level":3,"score":0.5001000165939331},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4749999940395355},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4691999852657318},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4602999985218048},{"id":"https://openalex.org/C2776141551","wikidata":"https://www.wikidata.org/wiki/Q16000087","display_name":"Emotion perception","level":3,"score":0.44839999079704285},{"id":"https://openalex.org/C90559484","wikidata":"https://www.wikidata.org/wiki/Q778379","display_name":"Expression (computer science)","level":2,"score":0.4375999867916107},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3855000138282776},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.3582000136375427},{"id":"https://openalex.org/C6438553","wikidata":"https://www.wikidata.org/wiki/Q1185804","display_name":"Affective computing","level":2,"score":0.33869999647140503},{"id":"https://openalex.org/C200288055","wikidata":"https://www.wikidata.org/wiki/Q2621792","display_name":"Element (criminal law)","level":2,"score":0.3052000105381012},{"id":"https://openalex.org/C206310091","wikidata":"https://www.wikidata.org/wiki/Q750859","display_name":"Emotion classification","level":2,"score":0.29989999532699585},{"id":"https://openalex.org/C195704467","wikidata":"https://www.wikidata.org/wiki/Q327968","display_name":"Facial expression","level":2,"score":0.2992999851703644},{"id":"https://openalex.org/C184408114","wikidata":"https://www.wikidata.org/wiki/Q1502022","display_name":"Generative Design","level":3,"score":0.29269999265670776},{"id":"https://openalex.org/C194969405","wikidata":"https://www.wikidata.org/wiki/Q170519","display_name":"Virtual reality","level":2,"score":0.28690001368522644},{"id":"https://openalex.org/C193293595","wikidata":"https://www.wikidata.org/wiki/Q23852","display_name":"Human body","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C128534915","wikidata":"https://www.wikidata.org/wiki/Q3475770","display_name":"Affective science","level":3,"score":0.2581000030040741},{"id":"https://openalex.org/C2986578859","wikidata":"https://www.wikidata.org/wiki/Q657632","display_name":"Human motion","level":3,"score":0.25619998574256897},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2549000084400177}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.28769","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28769","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.28769","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28769","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.4101879596710205,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Emotional":[0],"body":[1,45,77],"motion":[2,46,181],"expressions":[3,14],"are":[4],"an":[5,124],"essential":[6],"element":[7],"of":[8,18,59,92,137,159,167,195,208],"non-verbal":[9],"communication.":[10],"Effectively":[11],"conveying":[12],"these":[13],"through":[15],"technology":[16],"is":[17,54],"utmost":[19],"importance,":[20],"for":[21,40,170],"example,":[22],"with":[23,145,151],"virtual":[24],"reality":[25],"avatars":[26],"and":[27,64,139,183,205],"in":[28,33],"social":[29],"robotics.":[30],"Recent":[31],"advances":[32],"generative":[34,71,103,168,198],"models":[35],"have":[36],"opened":[37],"new":[38],"opportunities":[39],"advancing":[41],"research":[42],"on":[43,110],"emotional":[44,51,60,76,93],"learning.":[47],"However,":[48],"generating":[49],"accurate":[50],"expression":[52],"representations":[53],"challenging,":[55],"given":[56],"the":[57,117,165,193],"subtlety":[58],"cues,":[61],"individual":[62],"variability,":[63],"cultural":[65],"differences.":[66],"We":[67,115],"investigate":[68],"whether":[69],"a":[70,90,101,134,141,156],"model":[72,104],"can":[73],"implicitly":[74],"learn":[75],"motions":[78,108,119],"directly":[79],"from":[80,120],"culturally":[81],"grounded":[82],"motion-capture":[83],"data,":[84],"without":[85],"explicit":[86],"emotion-motion":[87],"guidance.":[88],"Using":[89],"dataset":[91],"performances":[94],"by":[95,130],"49":[96],"Japanese":[97,146],"actors,":[98],"we":[99,163],"trained":[100],"Transformer-based":[102],"to":[105,127,148,200],"generate":[106],"expressive":[107],"conditioned":[109],"13":[111],"discrete":[112],"emotion":[113,175,188,209],"labels.":[114],"evaluate":[116,164],"generated":[118],"two":[121],"perspectives:":[122],"(1)":[123],"LSTM-based":[125],"classifier":[126],"assess":[128,149],"recognizability":[129],"machine":[131],"observers,":[132],"achieving":[133],"recognition":[135,157,176],"accuracy":[136,158],"22.80%,":[138],"(2)":[140],"human":[142,152],"perception":[143],"study":[144],"raters":[147],"alignment":[150],"affective":[153,202],"interpretations,":[154],"yielding":[155],"24.91%.":[160],"Beyond":[161],"these,":[162],"utility":[166],"modeling":[169,199],"three":[171],"practical":[172],"tasks:":[173],"augmenting":[174],"models,":[177],"extracting":[178],"representative":[179],"emotion-specific":[180],"patterns,":[182],"synthesizing":[184],"smooth":[185],"transitions":[186],"between":[187],"intensities.":[189],"Our":[190],"findings":[191],"highlight":[192],"potential":[194],"implicit,":[196],"data-driven":[197],"enhance":[201],"computing":[203],"applications":[204],"our":[206],"understanding":[207],"expressions.":[210]},"counts_by_year":[],"updated_date":"2026-07-01T06:29:00.853634","created_date":"2026-07-01T00:00:00"}