{"id":"https://openalex.org/W7125695149","doi":"https://doi.org/10.48550/arxiv.2601.16763","title":"Flow Matching for Probabilistic Monocular 3D Human Pose Estimation","display_name":"Flow Matching for Probabilistic Monocular 3D Human Pose Estimation","publication_year":2026,"publication_date":"2026-01-23","ids":{"openalex":"https://openalex.org/W7125695149","doi":"https://doi.org/10.48550/arxiv.2601.16763"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.16763","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.16763","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2601.16763","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123861085","display_name":"Cuong Le","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Le, Cuong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123468131","display_name":"Pavlo Melnyk","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Melnyk, Pavlo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102907822","display_name":"Bastian Wandt","orcid":"https://orcid.org/0009-0002-1203-1093"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wandt, Bastian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Wadenb\u00e4ck, M\u00e5rten","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wadenb\u00e4ck, M\u00e5rten","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5123861085"],"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9861999750137329,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9861999750137329,"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/T12290","display_name":"Human Motion and Animation","score":0.004600000102072954,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.002099999925121665,"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/pose","display_name":"Pose","score":0.8011000156402588},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.7028999924659729},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.6399999856948853},{"id":"https://openalex.org/keywords/3d-pose-estimation","display_name":"3D pose estimation","score":0.6183000206947327},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5727999806404114},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5508000254631042},{"id":"https://openalex.org/keywords/articulated-body-pose-estimation","display_name":"Articulated body pose estimation","score":0.5378999710083008},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45750001072883606}],"concepts":[{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.8011000156402588},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7156999707221985},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7028999924659729},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.6399999856948853},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6287000179290771},{"id":"https://openalex.org/C36613465","wikidata":"https://www.wikidata.org/wiki/Q4636322","display_name":"3D pose estimation","level":3,"score":0.6183000206947327},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5999000072479248},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5727999806404114},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5508000254631042},{"id":"https://openalex.org/C22100474","wikidata":"https://www.wikidata.org/wiki/Q4800952","display_name":"Articulated body pose estimation","level":4,"score":0.5378999710083008},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45750001072883606},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43970000743865967},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.40209999680519104},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.33180001378059387},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.328900009393692},{"id":"https://openalex.org/C158829959","wikidata":"https://www.wikidata.org/wiki/Q1640606","display_name":"Monocular vision","level":2,"score":0.3262999951839447},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.31529998779296875},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.31200000643730164},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.31119999289512634},{"id":"https://openalex.org/C27458966","wikidata":"https://www.wikidata.org/wiki/Q1187693","display_name":"Control flow graph","level":2,"score":0.2957000136375427},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2639000117778778}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.16763","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.16763","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2601.16763","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.16763","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.4331624209880829}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recovering":[0],"3D":[1,22,31,42,66,99,181],"human":[2,23,67,100,122,182],"poses":[3],"from":[4,26,91],"a":[5,10,45,58,64,92],"monocular":[6],"camera":[7],"view":[8],"is":[9,111,202],"highly":[11],"ill-posed":[12],"problem":[13],"due":[14],"to":[15,96],"the":[16,35,41,50,54,73,80,83,88,97,118,126,142,152,194],"depth":[17],"ambiguity.":[18],"Earlier":[19],"studies":[20],"on":[21,72,79,176,193],"pose":[24,68,101,183],"lifting":[25,109],"2D":[27,81,108],"often":[28],"contain":[29],"incorrect-yet-overconfident":[30],"estimations.":[32],"To":[33],"mitigate":[34],"problem,":[36],"emerging":[37],"probabilistic":[38,65],"approaches":[39],"treat":[40],"estimations":[43],"as":[44,125],"distribution,":[46],"taking":[47],"into":[48],"account":[49],"uncertainty":[51],"measurement":[52],"of":[53,169],"poses.":[55],"Falling":[56],"in":[57,141],"similar":[59],"category,":[60],"we":[61],"proposed":[62],"FMPose,":[63],"estimation":[69],"method":[70],"based":[71],"flow":[74,84],"matching":[75,85],"generative":[76],"approach.":[77],"Conditioned":[78],"cues,":[82],"scheme":[86],"learns":[87],"optimal":[89],"transport":[90],"simple":[93],"source":[94],"distribution":[95,102],"plausible":[98],"via":[103,113],"continuous":[104],"normalizing":[105],"flows.":[106],"The":[107,199],"condition":[110],"modeled":[112],"graph":[114,127],"convolutional":[115],"networks,":[116],"leveraging":[117],"learnable":[119],"connections":[120],"between":[121,134],"body":[123],"joints":[124],"structure":[128],"for":[129,180],"feature":[130],"aggregation.":[131],"While":[132],"trade-offs":[133],"processing":[135,149],"time":[136,150],"and":[137,155,160],"precision":[138],"exist,":[139],"already":[140],"equal-accuracy":[143],"comparison,":[144],"FMPose":[145,171,189],"exhibits":[146],"significantly":[147],"faster":[148,159],"than":[151],"diffusion":[153],"model,":[154],"also":[156],"offers":[157],"another":[158],"more":[161,195],"accurate":[162],"configuration.":[163],"Experimental":[164],"results":[165],"show":[166],"major":[167],"improvements":[168],"our":[170],"over":[172],"current":[173],"state-of-the-art":[174],"methods":[175],"two":[177],"common":[178],"benchmarks":[179],"estimation,":[184],"namely":[185],"Human3.6M,":[186],"MPI-INF-3DHP.":[187],"Additionally,":[188],"shows":[190],"competitive":[191],"performance":[192],"challenging":[196],"3DPW":[197],"dataset.":[198],"code":[200],"implementation":[201],"available":[203],"at":[204],"https://github.com/cuongle1206/FMPose":[205]},"counts_by_year":[],"updated_date":"2026-05-27T06:14:07.914337","created_date":"2026-01-27T00:00:00"}