{"id":"https://openalex.org/W4386072501","doi":"https://doi.org/10.23919/mva57639.2023.10215966","title":"Human Pose Prediction by Progressive Generation in Multi-scale Frequency Domain","display_name":"Human Pose Prediction by Progressive Generation in Multi-scale Frequency Domain","publication_year":2023,"publication_date":"2023-07-23","ids":{"openalex":"https://openalex.org/W4386072501","doi":"https://doi.org/10.23919/mva57639.2023.10215966"},"language":"en","primary_location":{"id":"doi:10.23919/mva57639.2023.10215966","is_oa":false,"landing_page_url":"http://dx.doi.org/10.23919/mva57639.2023.10215966","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/A5100786583","display_name":"Tomohiro Fujita","orcid":"https://orcid.org/0000-0003-3352-4225"},"institutions":[{"id":"https://openalex.org/I4210108837","display_name":"Guardian Industries (United States)","ror":"https://ror.org/023x6j183","country_code":"US","type":"company","lineage":["https://openalex.org/I4210108837"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tomohiro Fujita","raw_affiliation_strings":["Guardian Robot Project R-IH, RIKEN,Kyoto,Japan","Guardian Robot Project R-IH, RIKEN, Kyoto, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guardian Robot Project R-IH, RIKEN,Kyoto,Japan","institution_ids":["https://openalex.org/I4210108837"]},{"raw_affiliation_string":"Guardian Robot Project R-IH, RIKEN, Kyoto, Japan","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027960360","display_name":"Yasutomo Kawanishi","orcid":"https://orcid.org/0000-0002-3799-4550"},"institutions":[{"id":"https://openalex.org/I4210108837","display_name":"Guardian Industries (United States)","ror":"https://ror.org/023x6j183","country_code":"US","type":"company","lineage":["https://openalex.org/I4210108837"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yasutomo Kawanishi","raw_affiliation_strings":["Guardian Robot Project R-IH, RIKEN,Kyoto,Japan","Guardian Robot Project R-IH, RIKEN, Kyoto, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guardian Robot Project R-IH, RIKEN,Kyoto,Japan","institution_ids":["https://openalex.org/I4210108837"]},{"raw_affiliation_string":"Guardian Robot Project R-IH, RIKEN, Kyoto, Japan","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1123,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.39681299,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"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":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":0.9988999962806702,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9987999796867371,"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/frequency-domain","display_name":"Frequency domain","score":0.6448021531105042},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6381017565727234},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5490612387657166},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.43627864122390747},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.385530561208725},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.19735196232795715},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10978665947914124},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06066522002220154}],"concepts":[{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.6448021531105042},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6381017565727234},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5490612387657166},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.43627864122390747},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.385530561208725},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.19735196232795715},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10978665947914124},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06066522002220154},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/mva57639.2023.10215966","is_oa":false,"landing_page_url":"http://dx.doi.org/10.23919/mva57639.2023.10215966","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":[{"score":0.5400000214576721,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1735317348","https://openalex.org/W2039024925","https://openalex.org/W2090229683","https://openalex.org/W2101032778","https://openalex.org/W2158164339","https://openalex.org/W2166063021","https://openalex.org/W2293741035","https://openalex.org/W2594167370","https://openalex.org/W2613864254","https://openalex.org/W2908684875","https://openalex.org/W2963548793","https://openalex.org/W2964203186","https://openalex.org/W2983925976","https://openalex.org/W3004407381","https://openalex.org/W3034696014","https://openalex.org/W3035545045","https://openalex.org/W3203785074","https://openalex.org/W4230178929","https://openalex.org/W6631190155","https://openalex.org/W6683128514","https://openalex.org/W6684451488"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W4402327032","https://openalex.org/W2382290278"],"abstract_inverted_index":{"We":[0,116],"address":[1],"a":[2,10,38,47,76,94,113],"problem":[3],"of":[4,12,46,78,85,112],"3D":[5],"human":[6,13,39,48,86,96],"pose":[7,97],"prediction":[8,63,98,123],"from":[9,75],"sequence":[11,50],"body":[14,87],"skeletons.":[15,88],"To":[16],"model":[17,99],"the":[18,21,27,55,62,79,82,119],"spatio-temporal":[19],"dynamics,":[20],"discrete":[22],"cosine":[23],"transform":[24],"(DCT)":[25],"and":[26,81,109,132],"graph":[28],"convolutional":[29],"networks":[30],"(GCN)":[31],"are":[32],"often":[33],"applied":[34],"to":[35],"signals":[36],"on":[37,130],"skeleton":[40,49],"graph.":[41],"By":[42],"DCT,":[43],"temporal":[44],"information":[45],"can":[51],"be":[52],"embedded":[53],"into":[54],"frequency":[56,70,101],"domain.":[57],"However,":[58],"in":[59,100],"previous":[60],"studies,":[61],"models":[64],"using":[65,127],"DCT":[66],"implicitly":[67],"learned":[68],"each":[69],"coe\ufb03cient":[71],"by":[72],"gradients":[73],"calculated":[74],"loss":[77],"predictions":[80],"ground":[83],"truths":[84],"In":[89],"this":[90],"paper,":[91],"we":[92],"propose":[93],"progressive":[95],"domain":[102],"so":[103],"that":[104,118],"explicitly":[105],"predict":[106],"high-,":[107],"medium-,":[108],"low-frequency":[110],"motion":[111],"target":[114],"person.":[115],"confirmed":[117],"proposed":[120],"method":[121],"improves":[122],"accuracy":[124],"through":[125],"experiments":[126],"public":[128],"datasets":[129],"Human3.6M":[131],"CMU":[133],"Mocap":[134],"datasets.":[135]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}