{"id":"https://openalex.org/W4385485679","doi":"https://doi.org/10.1145/3594315.3594340","title":"MGPose: A Graph Data Augmentation Framework for 3D Human Pose Estimation via Masking","display_name":"MGPose: A Graph Data Augmentation Framework for 3D Human Pose Estimation via Masking","publication_year":2023,"publication_date":"2023-03-17","ids":{"openalex":"https://openalex.org/W4385485679","doi":"https://doi.org/10.1145/3594315.3594340"},"language":"en","primary_location":{"id":"doi:10.1145/3594315.3594340","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3594315.3594340","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 9th International Conference on Computing and Artificial Intelligence","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/A5102018790","display_name":"Bai Zhang","orcid":"https://orcid.org/0009-0005-9771-6754"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bai Zhang","raw_affiliation_strings":["School of Artificial Intelligence, Jilin University, China and Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Ministry of Education, China"],"raw_orcid":"https://orcid.org/0009-0005-9771-6754","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Jilin University, China and Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Ministry of Education, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072415508","display_name":"Yixing Gao","orcid":"https://orcid.org/0000-0003-4475-2792"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yixing Gao","raw_affiliation_strings":["School of Artificial Intelligence, Jilin University, China and Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Ministry of Education, China"],"raw_orcid":"https://orcid.org/0000-0003-4475-2792","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Jilin University, China and Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Ministry of Education, China","institution_ids":["https://openalex.org/I194450716"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07859386,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"164","last_page":"170"},"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":0.9894000291824341,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.98580002784729,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7896692752838135},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6943581104278564},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6788546442985535},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.573691725730896},{"id":"https://openalex.org/keywords/offset","display_name":"Offset (computer science)","score":0.5720047354698181},{"id":"https://openalex.org/keywords/masking","display_name":"Masking (illustration)","score":0.5628291964530945},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5433725118637085},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.5287525057792664},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4870219826698303},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.44396811723709106},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4237423539161682},{"id":"https://openalex.org/keywords/factor-graph","display_name":"Factor graph","score":0.4104594588279724},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36370474100112915},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3545897603034973},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2917279303073883},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.29101020097732544},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2496260106563568},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.1726798415184021},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09864521026611328}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7896692752838135},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6943581104278564},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6788546442985535},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.573691725730896},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.5720047354698181},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.5628291964530945},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5433725118637085},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.5287525057792664},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4870219826698303},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.44396811723709106},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4237423539161682},{"id":"https://openalex.org/C159246509","wikidata":"https://www.wikidata.org/wiki/Q5428725","display_name":"Factor graph","level":3,"score":0.4104594588279724},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36370474100112915},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3545897603034973},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2917279303073883},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29101020097732544},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2496260106563568},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.1726798415184021},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09864521026611328},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3594315.3594340","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3594315.3594340","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 9th International Conference on Computing and Artificial Intelligence","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":23,"referenced_works":["https://openalex.org/W1943191679","https://openalex.org/W2101032778","https://openalex.org/W2612706635","https://openalex.org/W2891649471","https://openalex.org/W2948058585","https://openalex.org/W2962896489","https://openalex.org/W2964318832","https://openalex.org/W2972662547","https://openalex.org/W2989465897","https://openalex.org/W2994506732","https://openalex.org/W2998496395","https://openalex.org/W3035739162","https://openalex.org/W3092206109","https://openalex.org/W3097623574","https://openalex.org/W3098612954","https://openalex.org/W3104038788","https://openalex.org/W3158127560","https://openalex.org/W3169891778","https://openalex.org/W3183434010","https://openalex.org/W3202716970","https://openalex.org/W4210735024","https://openalex.org/W4289868074","https://openalex.org/W4299650397"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W2145836866","https://openalex.org/W2803255133","https://openalex.org/W2909431601"],"abstract_inverted_index":{"Currently,":[0],"the":[1,11,35,44,49,55,65,68,71,86,91,99,103,109,120,124,127,131,137,140,151,164],"scarcity":[2],"of":[3,13,22,67,90,123,133,139,146],"training":[4],"data":[5,29,101,105],"is":[6,95,148,155],"an":[7,40,96],"important":[8],"factor":[9],"restricting":[10],"performance":[12],"3D":[14],"human":[15,45],"pose":[16,37],"estimators.":[17],"To":[18],"make":[19],"better":[20],"use":[21],"existing":[23,36],"data,":[24],"we":[25,118],"propose":[26],"a":[27],"graph":[28],"augmentation":[30],"framework,":[31],"MGPose.":[32],"We":[33,160],"regard":[34],"estimator":[38],"as":[39],"autoencoder":[41],"architecture,":[42],"masking":[43],"joints":[46,69,76,125],"randomly":[47],"in":[48,54,126],"encoder":[50,61],"and":[51,102,129,158,169],"reconstructing":[52],"them":[53],"decoder.":[56],"Through":[57],"random":[58],"masking,":[59],"our":[60],"can":[62],"only":[63],"get":[64],"features":[66,122],"from":[70],"visible":[72,75],"subset.":[73],"The":[74,143],"obtained":[77],"each":[78],"time":[79],"are":[80],"different,":[81],"which":[82,154],"helps":[83],"to":[84,114,135,150],"improve":[85],"feature":[87],"extraction":[88],"ability":[89],"encoder.":[92,111],"However,":[93],"there":[94],"offset":[97],"between":[98],"masked":[100],"original":[104,152],"after":[106],"passing":[107],"through":[108],"same":[110],"In":[112],"order":[113],"narrow":[115],"this":[116],"gap,":[117],"reconstruct":[119],"high-dimensional":[121],"decoder":[128],"introduce":[130],"idea":[132],"equivariance":[134],"constrain":[136],"generation":[138],"joint":[141],"data.":[142],"parameter":[144],"scale":[145],"MGPose":[147],"comparable":[149],"model,":[153],"very":[156],"simple":[157],"efficient.":[159],"conduct":[161],"experiments":[162],"on":[163],"most":[165],"popular":[166],"dataset":[167],"Human3.6M":[168],"achieve":[170],"state-of-the-art":[171],"results.":[172]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}