{"id":"https://openalex.org/W7110305014","doi":"https://doi.org/10.1145/3757377.3763998","title":"Echo: Enhancing Conversational Behavior Generation via Hierarchical Semantic Comprehension with Large Language Models","display_name":"Echo: Enhancing Conversational Behavior Generation via Hierarchical Semantic Comprehension with Large Language Models","publication_year":2025,"publication_date":"2025-12-08","ids":{"openalex":"https://openalex.org/W7110305014","doi":"https://doi.org/10.1145/3757377.3763998"},"language":null,"primary_location":{"id":"doi:10.1145/3757377.3763998","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3757377.3763998","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the SIGGRAPH Asia 2025 Conference Papers","raw_type":"proceedings-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":null,"display_name":"Haiwei Xue","orcid":"https://orcid.org/0000-0001-7318-9682"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haiwei Xue","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yanbo Fan","orcid":"https://orcid.org/0000-0002-8530-485X"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]},{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanbo Fan","raw_affiliation_strings":["Nanjing University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University, Suzhou, China","institution_ids":["https://openalex.org/I3923682","https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xuan Wang","orcid":"https://orcid.org/0000-0001-5813-3875"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xuan Wang","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Zhiyong Wu","orcid":"https://orcid.org/0000-0001-8533-0524"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyong Wu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":2.3655,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.91127635,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.5002999901771545,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.5002999901771545,"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/T10709","display_name":"Social Robot Interaction and HRI","score":0.1826000064611435,"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/T12290","display_name":"Human Motion and Animation","score":0.08229999989271164,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/conversation","display_name":"Conversation","score":0.6746000051498413},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6539999842643738},{"id":"https://openalex.org/keywords/comprehension","display_name":"Comprehension","score":0.5777000188827515},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.49129998683929443},{"id":"https://openalex.org/keywords/language-understanding","display_name":"Language understanding","score":0.4767000079154968},{"id":"https://openalex.org/keywords/embodied-cognition","display_name":"Embodied cognition","score":0.4368000030517578},{"id":"https://openalex.org/keywords/dialog-system","display_name":"Dialog system","score":0.42559999227523804},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.41839998960494995}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8151000142097473},{"id":"https://openalex.org/C2777200299","wikidata":"https://www.wikidata.org/wiki/Q52943","display_name":"Conversation","level":2,"score":0.6746000051498413},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6539999842643738},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.5777000188827515},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5708000063896179},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.505299985408783},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.49129998683929443},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.4767000079154968},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.44769999384880066},{"id":"https://openalex.org/C100609095","wikidata":"https://www.wikidata.org/wiki/Q1335050","display_name":"Embodied cognition","level":2,"score":0.4368000030517578},{"id":"https://openalex.org/C190954187","wikidata":"https://www.wikidata.org/wiki/Q5270587","display_name":"Dialog system","level":3,"score":0.42559999227523804},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.41839998960494995},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41690000891685486},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.3986999988555908},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3779999911785126},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.3255999982357025},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3255000114440918},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.304500013589859},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.3043000102043152},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.29249998927116394},{"id":"https://openalex.org/C511149849","wikidata":"https://www.wikidata.org/wiki/Q7449051","display_name":"Semantic computing","level":3,"score":0.2797999978065491},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3757377.3763998","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3757377.3763998","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the SIGGRAPH Asia 2025 Conference Papers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.748164713382721}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1498883201","https://openalex.org/W1969962548","https://openalex.org/W2007337857","https://openalex.org/W2096178388","https://openalex.org/W2922298118","https://openalex.org/W2962795401","https://openalex.org/W3083173864","https://openalex.org/W3175246890","https://openalex.org/W4210802262","https://openalex.org/W4244535864","https://openalex.org/W4254522817","https://openalex.org/W4292945985","https://openalex.org/W4303448003","https://openalex.org/W4327519267","https://openalex.org/W4364377334","https://openalex.org/W4377010269","https://openalex.org/W4385284180","https://openalex.org/W4385993915","https://openalex.org/W4386065807","https://openalex.org/W4386270206","https://openalex.org/W4386655536","https://openalex.org/W4387421378","https://openalex.org/W4387421577","https://openalex.org/W4392910552","https://openalex.org/W4400822426","https://openalex.org/W4402703119","https://openalex.org/W4402716313","https://openalex.org/W4402727590","https://openalex.org/W4403498012","https://openalex.org/W4403704329","https://openalex.org/W4403717124","https://openalex.org/W4403791724","https://openalex.org/W4404966690","https://openalex.org/W4405833215","https://openalex.org/W4409261940"],"related_works":[],"abstract_inverted_index":{"Conversational":[0],"behavior":[1],"generation,":[2,54],"being":[3],"a":[4,11,169],"crucial":[5],"capability":[6],"of":[7,37,59,86,126],"embodied":[8],"agents,":[9],"is":[10],"significant":[12],"factor":[13],"influencing":[14],"human-computer":[15],"interaction.":[16],"Generating":[17],"high-quality":[18,227],"conversational":[19,38,61,93,101,115,143,159,228],"motions":[20,49,67,102],"requires":[21],"not":[22,70],"only":[23],"appropriate":[24,71],"audio-motion":[25,179],"mapping":[26],"but":[27],"also":[28],"interactive":[29],"responses":[30],"to":[31,64,90,106,152,181,187,221],"interlocutor":[32,48],"behaviors":[33],"and":[34,47,109,117,121,149,161,185,202],"comprehensive":[35,142],"understanding":[36,58,84,184],"semantics.":[39],"Existing":[40],"methods":[41],"primarily":[42],"rely":[43],"on":[44],"audio":[45,201],"signals":[46],"for":[50,72],"main":[51],"agent":[52],"motion":[53,203,218],"lacking":[55],"high-level":[56],"semantic":[57,83,111,164,183,194],"the":[60,73,81,114,118,127],"content,":[62],"leading":[63],"moderate":[65],"quality":[66],"that":[68,100,136,174,208],"are":[69,103],"dialogue.":[74],"To":[75,140],"address":[76],"these":[77],"limitations,":[78],"we":[79,129,167],"leverage":[80],"powerful":[82],"capabilities":[85],"large":[87,154,197],"language":[88,155,198],"models,":[89],"comprehend":[91],"complex":[92],"contexts.":[94],"Inspired":[95],"by":[96],"human":[97],"conversation":[98],"processes":[99],"highly":[104],"related":[105],"both":[107],"global":[108],"local":[110],"factors,":[112],"including":[113],"context,":[116],"intentions,":[119],"emotions,":[120],"passive":[122],"or":[123],"active":[124],"states":[125],"participants,":[128],"propose":[130],"an":[131],"agentic":[132],"system":[133],"named":[134],"Echo":[135,145],"analyzes":[137],"such":[138],"information.":[139,165],"achieve":[141],"understanding,":[144],"leverages":[146],"multiple":[147],"prompts":[148],"test-time":[150],"recipes":[151],"guide":[153],"models":[156,199,220],"in":[157,225],"decomposing":[158],"structures":[160],"extracting":[162],"fine-grained":[163,193],"Furthermore,":[166],"design":[168],"hierarchical":[170],"feature":[171],"fusion":[172],"network":[173],"systematically":[175],"integrates":[176],"from":[177,196],"frame-level":[178],"features":[180,195],"sentence-level":[182],"finally":[186],"conversation-level":[188],"contextual":[189],"comprehension,":[190],"organically":[191],"combining":[192],"with":[200,215],"characteristics.":[204],"Experimental":[205],"results":[206],"demonstrate":[207],"our":[209],"framework":[210],"can":[211],"be":[212],"effectively":[213],"integrated":[214],"several":[216],"state-of-the-art":[217],"generation":[219],"enhance":[222],"their":[223],"performance":[224],"generating":[226],"behaviors.":[229]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-12-08T00:00:00"}