{"id":"https://openalex.org/W4386067005","doi":"https://doi.org/10.23919/mva57639.2023.10216076","title":"Intra-frame Skeleton Constraints Modeling and Grouping Strategy Based Multi-Scale Graph Convolution Network for 3D Human Motion Prediction","display_name":"Intra-frame Skeleton Constraints Modeling and Grouping Strategy Based Multi-Scale Graph Convolution Network for 3D Human Motion Prediction","publication_year":2023,"publication_date":"2023-07-23","ids":{"openalex":"https://openalex.org/W4386067005","doi":"https://doi.org/10.23919/mva57639.2023.10216076"},"language":"en","primary_location":{"id":"doi:10.23919/mva57639.2023.10216076","is_oa":false,"landing_page_url":"http://dx.doi.org/10.23919/mva57639.2023.10216076","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 18th International Conference on Machine Vision and Applications (MVA)","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/A5102547709","display_name":"Zhihan Zhuang","orcid":"https://orcid.org/0009-0003-7944-4498"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Zhihan Zhuang","raw_affiliation_strings":["Waseda University,Graduate School of Information, Production and Systems,Kitakyushu,Japan,808-0135"],"affiliations":[{"raw_affiliation_string":"Waseda University,Graduate School of Information, Production and Systems,Kitakyushu,Japan,808-0135","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100390903","display_name":"Yuan Li","orcid":"https://orcid.org/0000-0002-8479-3049"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yuan Li","raw_affiliation_strings":["Waseda University,Graduate School of Information, Production and Systems,Kitakyushu,Japan,808-0135"],"affiliations":[{"raw_affiliation_string":"Waseda University,Graduate School of Information, Production and Systems,Kitakyushu,Japan,808-0135","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033755910","display_name":"\u0421\u043e\u043d\u0433\u043b\u0438\u043d \u0414\u0443","orcid":"https://orcid.org/0000-0003-4937-2713"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Songlin Du","raw_affiliation_strings":["Southeast University,Nanjing,China","Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Southeast University,Nanjing,China","institution_ids":["https://openalex.org/I76569877"]},{"raw_affiliation_string":"Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103206427","display_name":"Takeshi Ikenaga","orcid":"https://orcid.org/0000-0001-8338-8175"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takeshi Ikenaga","raw_affiliation_strings":["Waseda University,Graduate School of Information, Production and Systems,Kitakyushu,Japan,808-0135"],"affiliations":[{"raw_affiliation_string":"Waseda University,Graduate School of Information, Production and Systems,Kitakyushu,Japan,808-0135","institution_ids":["https://openalex.org/I150744194"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5102547709"],"corresponding_institution_ids":["https://openalex.org/I150744194"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09362035,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T12290","display_name":"Human Motion and Animation","score":0.9940999746322632,"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/computer-science","display_name":"Computer science","score":0.7128655910491943},{"id":"https://openalex.org/keywords/adjacency-matrix","display_name":"Adjacency matrix","score":0.617326557636261},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6112844944000244},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5971654057502747},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5559791326522827},{"id":"https://openalex.org/keywords/skeleton","display_name":"Skeleton (computer programming)","score":0.5434709787368774},{"id":"https://openalex.org/keywords/adjacency-list","display_name":"Adjacency list","score":0.5357847213745117},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5342490077018738},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4417249858379364},{"id":"https://openalex.org/keywords/motion-capture","display_name":"Motion capture","score":0.4367789924144745},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3806130290031433},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.359512597322464},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.34006965160369873},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2711320221424103},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.18055692315101624}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7128655910491943},{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.617326557636261},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6112844944000244},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5971654057502747},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5559791326522827},{"id":"https://openalex.org/C18969341","wikidata":"https://www.wikidata.org/wiki/Q1169129","display_name":"Skeleton (computer programming)","level":2,"score":0.5434709787368774},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.5357847213745117},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5342490077018738},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4417249858379364},{"id":"https://openalex.org/C48007421","wikidata":"https://www.wikidata.org/wiki/Q676252","display_name":"Motion capture","level":3,"score":0.4367789924144745},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3806130290031433},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.359512597322464},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34006965160369873},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2711320221424103},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.18055692315101624},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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:10.23919/mva57639.2023.10216076","is_oa":false,"landing_page_url":"http://dx.doi.org/10.23919/mva57639.2023.10216076","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 18th International Conference on Machine Vision and Applications (MVA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1735317348","https://openalex.org/W2110935301","https://openalex.org/W2483862638","https://openalex.org/W2964203186","https://openalex.org/W2964304707","https://openalex.org/W2983925976","https://openalex.org/W3109717189","https://openalex.org/W4220911514","https://openalex.org/W4221142137","https://openalex.org/W4230178929","https://openalex.org/W4304080460","https://openalex.org/W4312384811","https://openalex.org/W4385245566","https://openalex.org/W6676977166","https://openalex.org/W6697324800","https://openalex.org/W6722076910","https://openalex.org/W6739901393","https://openalex.org/W6781317771","https://openalex.org/W6839660704"],"related_works":["https://openalex.org/W4213150077","https://openalex.org/W2369410163","https://openalex.org/W2059018062","https://openalex.org/W2604585036","https://openalex.org/W2078477160","https://openalex.org/W1989103179","https://openalex.org/W2089681211","https://openalex.org/W1991172810","https://openalex.org/W125803343","https://openalex.org/W2153421018"],"abstract_inverted_index":{"Attention-based":[0],"feed-forward":[1],"networks":[2,6],"and":[3,25,60,81,100,151,170,175],"graph":[4,54],"convolution":[5,55],"have":[7,31],"recently":[8],"shown":[9],"great":[10],"promise":[11],"in":[12,22,41,113,136,146],"3D":[13],"skeleton-based":[14],"human":[15,143],"motion":[16,62],"prediction":[17,169,174],"for":[18,38,167,172],"their":[19],"good":[20],"performance":[21],"learning":[23],"temporal":[24],"spatial":[26,36],"relations.":[27],"However,":[28],"previous":[29],"methods":[30],"two":[32],"critical":[33],"issues:":[34],"first,":[35],"dependencies":[37],"distal":[39,111],"joints":[40,112],"each":[42,114],"independent":[43,115],"frame":[44],"are":[45],"hard":[46],"to":[47,105,123],"learn;":[48],"second,":[49],"the":[50,107,125,137,178],"basic":[51],"architecture":[52],"of":[53,64,110,127,133,165],"network":[56],"ignores":[57],"hierarchical":[58],"structure":[59],"diverse":[61],"patterns":[63],"different":[65,147],"body":[66,129,144],"parts.":[67],"To":[68],"address":[69],"these":[70],"issues,":[71],"this":[72],"paper":[73],"proposes":[74],"an":[75,162],"intra-frame":[76,92],"skeleton":[77,93,108],"constraints":[78,94,109],"modeling":[79,95],"method":[80,96,160],"a":[82,101,120],"Grouping":[83],"based":[84],"Multi-Scale":[85],"Graph":[86],"Convolution":[87],"Network":[88],"(GMS-GCN)":[89],"model.":[90],"The":[91,117],"leverages":[97],"self-attention":[98],"mechanism":[99],"designed":[102],"adjacency":[103],"matrix":[104],"model":[106],"frame.":[116],"GMS-GCN":[118,141],"utilizes":[119],"grouping":[121],"strategy":[122],"learn":[124],"dynamics":[126],"various":[128],"parts":[130],"separately.":[131],"Instead":[132],"mapping":[134],"features":[135,145],"same":[138],"feature":[139],"space,":[140],"extracts":[142],"dimensions":[148],"by":[149],"up-sample":[150],"down-sample":[152],"GCN":[153],"layers.":[154],"Experiment":[155],"results":[156],"demonstrate":[157],"that":[158],"our":[159],"achieves":[161],"average":[163],"MPJPE":[164],"34.7mm":[166],"short-term":[168],"93.2mm":[171],"long-term":[173],"both":[176],"outperform":[177],"state-of-the-art":[179],"approaches.":[180]},"counts_by_year":[],"updated_date":"2025-12-24T23:09:58.560324","created_date":"2025-10-10T00:00:00"}