{"id":"https://openalex.org/W4304014819","doi":"https://doi.org/10.1145/3503161.3547846","title":"Dynamic Graph Reasoning for Multi-person 3D Pose Estimation","display_name":"Dynamic Graph Reasoning for Multi-person 3D Pose Estimation","publication_year":2022,"publication_date":"2022-10-10","ids":{"openalex":"https://openalex.org/W4304014819","doi":"https://doi.org/10.1145/3503161.3547846"},"language":"en","primary_location":{"id":"doi:10.1145/3503161.3547846","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3547846","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Multimedia","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":"https://openalex.org/A5086179961","display_name":"Zhongwei Qiu","orcid":null},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongwei Qiu","raw_affiliation_strings":["University of Science and Technology Beijing, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Science and Technology Beijing, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071036534","display_name":"Qiansheng Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiansheng Yang","raw_affiliation_strings":["Baidu, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Baidu, Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100696824","display_name":"Jian Wang","orcid":"https://orcid.org/0000-0002-4316-932X"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Wang","raw_affiliation_strings":["Baidu, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Baidu, Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016681542","display_name":"Dongmei Fu","orcid":"https://orcid.org/0000-0003-3918-9448"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongmei Fu","raw_affiliation_strings":["University of Science and Technology Beijing, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Science and Technology Beijing, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7668,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.80577838,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3521","last_page":"3529"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":1.0,"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":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9937999844551086,"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/decoding-methods","display_name":"Decoding methods","score":0.8469831943511963},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7310221791267395},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.5917234420776367},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5800732970237732},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.47299084067344666},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4668472707271576},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4594569802284241},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.44953256845474243},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39430275559425354},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.34937965869903564},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.29314813017845154}],"concepts":[{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.8469831943511963},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7310221791267395},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.5917234420776367},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5800732970237732},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.47299084067344666},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4668472707271576},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4594569802284241},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.44953256845474243},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39430275559425354},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.34937965869903564},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29314813017845154},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3503161.3547846","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3547846","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W2032618685","https://openalex.org/W2080873731","https://openalex.org/W2101032778","https://openalex.org/W2307770531","https://openalex.org/W2554247908","https://openalex.org/W2557698284","https://openalex.org/W2559085405","https://openalex.org/W2604375920","https://openalex.org/W2605947573","https://openalex.org/W2612706635","https://openalex.org/W2771965516","https://openalex.org/W2792747672","https://openalex.org/W2795089319","https://openalex.org/W2798411580","https://openalex.org/W2798646183","https://openalex.org/W2891377836","https://openalex.org/W2916798096","https://openalex.org/W2963076818","https://openalex.org/W2963402313","https://openalex.org/W2963598138","https://openalex.org/W2963995996","https://openalex.org/W2964016027","https://openalex.org/W2964221239","https://openalex.org/W2964291722","https://openalex.org/W2964318832","https://openalex.org/W2965348796","https://openalex.org/W2966735886","https://openalex.org/W2972662547","https://openalex.org/W2997002052","https://openalex.org/W3035129432","https://openalex.org/W3035225512","https://openalex.org/W3098612954","https://openalex.org/W3106882556","https://openalex.org/W3107944344","https://openalex.org/W3109769043","https://openalex.org/W3116592456","https://openalex.org/W3189677397","https://openalex.org/W3203617912","https://openalex.org/W4232988217"],"related_works":["https://openalex.org/W2353179089","https://openalex.org/W2923538289","https://openalex.org/W2353125546","https://openalex.org/W2470643824","https://openalex.org/W4400595174","https://openalex.org/W2349635380","https://openalex.org/W4353089801","https://openalex.org/W2353819554","https://openalex.org/W2359488321","https://openalex.org/W2389866386"],"abstract_inverted_index":{"Multi-person":[0,76],"3D":[1,57,77,169,246],"pose":[2,78,247],"estimation":[3,79],"is":[4,89,161],"a":[5,60,64,106],"challenging":[6],"task":[7],"because":[8],"of":[9,18,92,187,219],"occlusion":[10,221],"and":[11,102,108,118,216,222,240],"depth":[12,109,223],"ambiguity,":[13],"especially":[14],"in":[15,87],"the":[16,75,144,148,168,185,188,203,213,232],"cases":[17],"crowd":[19],"scenes.":[20],"To":[21],"solve":[22],"these":[23,46,128],"problems,":[24],"most":[25],"existing":[26],"methods":[27,47,239],"explore":[28],"modeling":[29],"body":[30],"context":[31],"cues":[32],"by":[33,139,192],"enhancing":[34],"feature":[35],"representation":[36],"with":[37,63,80,105],"graph":[38,86,164,190],"neural":[39],"networks":[40],"or":[41],"adding":[42],"structural":[43],"constraints.":[44],"However,":[45],"are":[48,125,137,151,171],"not":[49],"robust":[50],"for":[51,122,178],"their":[52],"single-root":[53],"formulation":[54],"that":[55,231],"decoding":[56,85,120,135,145,176,189,204],"poses":[58,170],"from":[59,127,153],"root":[61,116],"node":[62],"pre-defined":[65],"graph.":[66],"In":[67,94],"this":[68,159],"paper,":[69],"we":[70],"propose":[71],"GR-M3D,":[72],"which":[73,201],"models":[74],"dynamic":[81,134,163,175],"Graph":[82],"Reasoning.":[83],"The":[84],"GR-M3D":[88,182],"predicted":[90],"instead":[91],"pre-defined.":[93],"particular,":[95],"It":[96],"firstly":[97],"generates":[98],"several":[99],"data":[100,129,156],"maps":[101],"enhances":[103],"them":[104],"scale":[107],"aware":[110],"refinement":[111],"module":[112],"(SDAR).":[113],"Then":[114],"multiple":[115],"keypoints":[117],"dense":[119],"paths":[121],"each":[123,179],"person":[124],"estimated":[126],"maps.":[130,157],"Based":[131],"on":[132,244],"them,":[133],"graphs":[136,177,205],"built":[138],"assigning":[140],"path":[141,149,195],"weights":[142,150,196],"to":[143,174,198,208,212],"paths,":[146],"while":[147],"inferred":[152],"those":[154],"enhanced":[155],"And":[158],"process":[160],"named":[162],"reasoning":[165],"(DGR).":[166],"Finally,":[167],"decoded":[172],"according":[173,197],"detected":[180],"person.":[181],"can":[183],"adjust":[184],"structure":[186],"implicitly":[191],"adopting":[193],"soft":[194],"input":[199,210],"data,":[200],"makes":[202],"be":[206],"adaptive":[207],"different":[209],"persons":[211],"best":[214],"extent":[215],"more":[217],"capable":[218],"handling":[220],"ambiguity":[224],"than":[225],"previous":[226],"methods.":[227],"We":[228],"empirically":[229],"show":[230],"proposed":[233],"bottom-up":[234],"approach":[235],"even":[236],"outperforms":[237],"top-down":[238],"achieves":[241],"state-of-the-art":[242],"results":[243],"three":[245],"datasets.":[248]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":5}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}