{"id":"https://openalex.org/W2059549660","doi":"https://doi.org/10.1145/1111411.1111418","title":"Human motion estimation from a reduced marker set","display_name":"Human motion estimation from a reduced marker set","publication_year":2006,"publication_date":"2006-01-01","ids":{"openalex":"https://openalex.org/W2059549660","doi":"https://doi.org/10.1145/1111411.1111418","mag":"2059549660"},"language":"en","primary_location":{"id":"doi:10.1145/1111411.1111418","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1111411.1111418","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2006 symposium on Interactive 3D graphics and games  - SI3D '06","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/A5100452284","display_name":"Guodong Liu","orcid":"https://orcid.org/0000-0003-1063-208X"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Guodong Liu","raw_affiliation_strings":["University of North Carolina at Chapel Hill"],"affiliations":[{"raw_affiliation_string":"University of North Carolina at Chapel Hill","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101766174","display_name":"Jingdan Zhang","orcid":"https://orcid.org/0009-0006-1038-7589"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jingdan Zhang","raw_affiliation_strings":["University of North Carolina at Chapel Hill"],"affiliations":[{"raw_affiliation_string":"University of North Carolina at Chapel Hill","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100392089","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0002-8180-2886"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["University of North Carolina at Chapel Hill"],"affiliations":[{"raw_affiliation_string":"University of North Carolina at Chapel Hill","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042463953","display_name":"Leonard McMillan","orcid":"https://orcid.org/0000-0002-8453-0847"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Leonard McMillan","raw_affiliation_strings":["University of North Carolina at Chapel Hill"],"affiliations":[{"raw_affiliation_string":"University of North Carolina at Chapel Hill","institution_ids":["https://openalex.org/I114027177"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100452284"],"corresponding_institution_ids":["https://openalex.org/I114027177"],"apc_list":null,"apc_paid":null,"fwci":4.9833,"has_fulltext":false,"cited_by_count":47,"citation_normalized_percentile":{"value":0.95749453,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"35","last_page":"35"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9998000264167786,"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/T11439","display_name":"Video Analysis and Summarization","score":0.9980000257492065,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7007264494895935},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6497437953948975},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6332160234451294},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5599488019943237},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5556094646453857},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5539638996124268},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5112014412879944},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.4677835702896118},{"id":"https://openalex.org/keywords/motion-capture","display_name":"Motion capture","score":0.4398460388183594},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4369047284126282},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.4193616211414337},{"id":"https://openalex.org/keywords/linear-classifier","display_name":"Linear classifier","score":0.4153575599193573},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4153035879135132},{"id":"https://openalex.org/keywords/piecewise-linear-function","display_name":"Piecewise linear function","score":0.4150197207927704},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.3894628882408142},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19280073046684265}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7007264494895935},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6497437953948975},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6332160234451294},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5599488019943237},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5556094646453857},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5539638996124268},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5112014412879944},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4677835702896118},{"id":"https://openalex.org/C48007421","wikidata":"https://www.wikidata.org/wiki/Q676252","display_name":"Motion capture","level":3,"score":0.4398460388183594},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4369047284126282},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4193616211414337},{"id":"https://openalex.org/C139532973","wikidata":"https://www.wikidata.org/wiki/Q2679259","display_name":"Linear classifier","level":3,"score":0.4153575599193573},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4153035879135132},{"id":"https://openalex.org/C17095337","wikidata":"https://www.wikidata.org/wiki/Q2375229","display_name":"Piecewise linear function","level":2,"score":0.4150197207927704},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.3894628882408142},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19280073046684265},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1111411.1111418","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1111411.1111418","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2006 symposium on Interactive 3D graphics and games  - SI3D '06","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1507699974","https://openalex.org/W1696716473","https://openalex.org/W1803418553","https://openalex.org/W1982905909","https://openalex.org/W1991942383","https://openalex.org/W2001141328","https://openalex.org/W2027913322","https://openalex.org/W2029903115","https://openalex.org/W2051567001","https://openalex.org/W2067502459","https://openalex.org/W2078127241","https://openalex.org/W2097944344","https://openalex.org/W2103633133","https://openalex.org/W2120051910","https://openalex.org/W2125027820","https://openalex.org/W2141104481","https://openalex.org/W2151391352","https://openalex.org/W2151857564","https://openalex.org/W2158268505","https://openalex.org/W2165870892","https://openalex.org/W2169779569","https://openalex.org/W2243558602","https://openalex.org/W2248685949","https://openalex.org/W2249800031","https://openalex.org/W2293741035","https://openalex.org/W2336236730","https://openalex.org/W2911964244"],"related_works":["https://openalex.org/W2345184372","https://openalex.org/W2055840562","https://openalex.org/W2129101332","https://openalex.org/W1964761968","https://openalex.org/W2326127771","https://openalex.org/W2362981726","https://openalex.org/W2151943039","https://openalex.org/W1981959369","https://openalex.org/W69190121","https://openalex.org/W2131377090"],"abstract_inverted_index":{"Motion":[0],"capture":[1,71],"data":[2],"from":[3,88,109,200],"human":[4,75,173],"subjects":[5],"exhibits":[6],"considerable":[7],"redundancy.":[8,19],"In":[9,20],"this":[10,18,52,63],"paper,":[11],"we":[12,22],"propose":[13],"novel":[14],"methods":[15],"for":[16,146],"exploiting":[17],"particular,":[21],"set":[23,55,179],"out":[24],"to":[25,35,56,70,82,123,193],"find":[26],"a":[27,48,78,89,106,128,135,149,156,160,176,196],"subset":[28,64],"of":[29,41,65,100,111,130,141,148,198],"motion-capture":[30],"markers":[31,67],"that":[32,50,62],"are":[33],"able":[34],"provide":[36],"fast":[37],"and":[38,115,182],"high-quality":[39],"predictions":[40],"the":[42,58,112,125,184],"remaining":[43],"markers.":[44],"We":[45,60,93,103,187],"then":[46,104],"develop":[47],"model":[49,145],"uses":[51],"reduced":[53,161,177],"marker":[54,162,178],"predict":[57],"others.":[59],"demonstrate":[61,189],"original":[66],"is":[68,152],"sufficient":[69],"subtle":[72],"variations":[73],"in":[74],"motion.We":[76],"take":[77],"data-driven":[79],"modeling":[80,121],"approach":[81],"learn":[83],"piecewise":[84],"local":[85,131],"linear":[86,132,144],"models":[87,133],"marker-based":[90],"training":[91],"set.":[92,163],"first":[94],"divide":[95],"motion":[96,113,174],"sequences":[97],"into":[98,127],"segments":[99,114,126],"low":[101],"dimensionality.":[102],"retrieve":[105],"feature":[107,118],"vector":[108],"each":[110],"use":[116],"these":[117],"vectors":[119],"as":[120],"primitives":[122],"cluster":[124],"hierarchy":[129],"via":[134,155],"divisive":[136],"clustering":[137],"method.":[138],"The":[139],"selection":[140],"an":[142],"appropriate":[143],"reconstruction":[147],"full-body":[150,172],"pose":[151],"determined":[153],"automatically":[154],"classifier":[157],"driven":[158],"by":[159],"After":[164],"offline":[165],"training,":[166],"our":[167,190],"method":[168],"can":[169],"quickly":[170],"reconstruct":[171],"using":[175],"without":[180],"storing":[181],"searching":[183],"large":[185],"database.":[186],"also":[188],"method's":[191],"ability":[192],"generalize":[194],"over":[195],"variety":[197],"motions":[199],"multiple":[201],"subjects.":[202]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":2},{"year":2017,"cited_by_count":2},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":4},{"year":2012,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
