{"id":"https://openalex.org/W7154344290","doi":"https://doi.org/10.48550/arxiv.2604.11083","title":"FlowCoMotion: Text-to-Motion Generation via Token-Latent Flow Modeling","display_name":"FlowCoMotion: Text-to-Motion Generation via Token-Latent Flow Modeling","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7154344290","doi":"https://doi.org/10.48550/arxiv.2604.11083"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.11083","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11083","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2604.11083","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133626004","display_name":"Dawei Guan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guan, Dawei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133604004","display_name":"Di Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Di","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133568645","display_name":"Chengjie Jin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Chengjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133590803","display_name":"Jiangtao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jiangtao","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/T12290","display_name":"Human Motion and Animation","score":0.8449000120162964,"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"}},"topics":[{"id":"https://openalex.org/T12290","display_name":"Human Motion and Animation","score":0.8449000120162964,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.06870000064373016,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.02250000089406967,"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/ode","display_name":"Ode","score":0.5634999871253967},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.5286999940872192},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.48570001125335693},{"id":"https://openalex.org/keywords/solver","display_name":"Solver","score":0.4553000032901764},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.4287000000476837},{"id":"https://openalex.org/keywords/coupling","display_name":"Coupling (piping)","score":0.40139999985694885},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.34380000829696655},{"id":"https://openalex.org/keywords/motion-capture","display_name":"Motion capture","score":0.3393000066280365}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6100999712944031},{"id":"https://openalex.org/C34862557","wikidata":"https://www.wikidata.org/wiki/Q178985","display_name":"Ode","level":2,"score":0.5634999871253967},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.5286999940872192},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.48570001125335693},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.484499990940094},{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.4553000032901764},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.4287000000476837},{"id":"https://openalex.org/C131584629","wikidata":"https://www.wikidata.org/wiki/Q4308705","display_name":"Coupling (piping)","level":2,"score":0.40139999985694885},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.367900013923645},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.34380000829696655},{"id":"https://openalex.org/C48007421","wikidata":"https://www.wikidata.org/wiki/Q676252","display_name":"Motion capture","level":3,"score":0.3393000066280365},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.33730000257492065},{"id":"https://openalex.org/C91188154","wikidata":"https://www.wikidata.org/wiki/Q186247","display_name":"Vector field","level":2,"score":0.33649998903274536},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.32820001244544983},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.3021000027656555},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.30140000581741333},{"id":"https://openalex.org/C145565327","wikidata":"https://www.wikidata.org/wiki/Q852514","display_name":"Motion control","level":3,"score":0.2842000126838684},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2808000147342682},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26510000228881836},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C10161872","wikidata":"https://www.wikidata.org/wiki/Q557891","display_name":"Motion estimation","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.11083","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11083","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2604.11083","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11083","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Text-to-motion":[0],"generation":[1,46],"is":[2,103,123],"driven":[3],"by":[4,106],"learning":[5],"motion":[6,21,35,45,68,101],"representations":[7,25,32,109],"for":[8],"semantic":[9,64,98],"alignment":[10],"with":[11,28],"language.":[12],"Existing":[13],"methods":[14],"rely":[15],"on":[16,126,161],"either":[17],"continuous":[18,24,81],"or":[19],"discrete":[20,31,91],"representations.":[22],"However,":[23],"entangle":[26],"semantics":[27],"dynamics,":[29],"while":[30,84],"lose":[33],"fine-grained":[34],"details.":[36,69],"In":[37,70],"this":[38,134],"context,":[39],"we":[40,74,89],"propose":[41],"FlowCoMotion,":[42],"a":[43,53,115,120,138],"novel":[44],"framework":[47],"that":[48,156],"unifies":[49],"both":[50,63],"treatments":[51],"from":[52,110,137],"modeling":[54],"perspective.":[55],"Specifically,":[56],"FlowCoMotion":[57,157],"employs":[58],"token-latent":[59,116],"coupling":[60,117],"to":[61,78,95,145],"capture":[62],"content":[65],"and":[66,166],"high-fidelity":[67],"the":[71,80,86,108,111,127,143,146,150],"latent":[72,82,102],"branch,":[73],"apply":[75],"multi-view":[76],"distillation":[77],"regularize":[79],"space,":[83],"in":[85],"token":[87],"branch":[88],"use":[90],"temporal":[92],"resolution":[93],"quantization":[94],"extract":[96],"high-level":[97],"cues.":[99],"The":[100],"then":[104],"obtained":[105],"combining":[107],"two":[112],"branches":[113],"through":[114],"network.":[118],"Subsequently,":[119],"velocity":[121,135],"field":[122,136],"predicted":[124],"based":[125],"textual":[128],"conditions.":[129],"An":[130],"ODE":[131],"solver":[132],"integrates":[133],"simple":[139],"prior,":[140],"thereby":[141],"guiding":[142],"sample":[144],"potential":[147],"state":[148],"of":[149],"target":[151],"motion.":[152],"Extensive":[153],"experiments":[154],"show":[155],"achieves":[158],"competitive":[159],"performance":[160],"text-to-motion":[162],"benchmarks,":[163],"including":[164],"HumanML3D":[165],"SnapMoGen.":[167]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-15T00:00:00"}
