{"id":"https://openalex.org/W3189555854","doi":"https://doi.org/10.24963/ijcai.2021/118","title":"Learning 3-D Human Pose Estimation from Catadioptric Videos","display_name":"Learning 3-D Human Pose Estimation from Catadioptric Videos","publication_year":2021,"publication_date":"2021-08-01","ids":{"openalex":"https://openalex.org/W3189555854","doi":"https://doi.org/10.24963/ijcai.2021/118","mag":"3189555854"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2021/118","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2021/118","pdf_url":"https://www.ijcai.org/proceedings/2021/0118.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2021/0118.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100767209","display_name":"Chenchen Liu","orcid":"https://orcid.org/0009-0007-0839-3290"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chenchen Liu","raw_affiliation_strings":["Peking University"],"affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100645829","display_name":"Yongzhi Li","orcid":"https://orcid.org/0000-0003-1586-8999"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongzhi Li","raw_affiliation_strings":["Peking University"],"affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048130388","display_name":"Kangqi Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kangqi Ma","raw_affiliation_strings":["Peking University"],"affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100461312","display_name":"Duo Zhang","orcid":"https://orcid.org/0000-0003-4895-2636"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Duo Zhang","raw_affiliation_strings":["Peking University"],"affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044605066","display_name":"Peijun Bao","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peijun Bao","raw_affiliation_strings":["Peking University"],"affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028877572","display_name":"Yadong Mu","orcid":"https://orcid.org/0000-0001-7815-3750"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yadong Mu","raw_affiliation_strings":["Peking University"],"affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100767209"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":0.2914,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.55680111,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"852","last_page":"859"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9961000084877014,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9941999912261963,"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/pose","display_name":"Pose","score":0.7866750955581665},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7656975984573364},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7064307928085327},{"id":"https://openalex.org/keywords/epipolar-geometry","display_name":"Epipolar geometry","score":0.609754741191864},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5862815976142883},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5114651918411255},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.48372820019721985},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.41550692915916443},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.41323548555374146},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.20885872840881348},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.11606928706169128}],"concepts":[{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.7866750955581665},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7656975984573364},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7064307928085327},{"id":"https://openalex.org/C23379248","wikidata":"https://www.wikidata.org/wiki/Q200904","display_name":"Epipolar geometry","level":3,"score":0.609754741191864},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5862815976142883},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5114651918411255},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.48372820019721985},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.41550692915916443},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.41323548555374146},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.20885872840881348},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.11606928706169128},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2021/118","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2021/118","pdf_url":"https://www.ijcai.org/proceedings/2021/0118.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2021/118","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2021/118","pdf_url":"https://www.ijcai.org/proceedings/2021/0118.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2492716755","display_name":null,"funder_award_id":"61772037","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3189555854.pdf"},"referenced_works_count":47,"referenced_works":["https://openalex.org/W1517129425","https://openalex.org/W1598351315","https://openalex.org/W1861492603","https://openalex.org/W1984190176","https://openalex.org/W2004987096","https://openalex.org/W2027657790","https://openalex.org/W2033819227","https://openalex.org/W2034043440","https://openalex.org/W2080873731","https://openalex.org/W2085261163","https://openalex.org/W2088230067","https://openalex.org/W2099333815","https://openalex.org/W2101032778","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2340779594","https://openalex.org/W2511444663","https://openalex.org/W2545173102","https://openalex.org/W2554247908","https://openalex.org/W2559085405","https://openalex.org/W2559320744","https://openalex.org/W2565639579","https://openalex.org/W2612706635","https://openalex.org/W2769331938","https://openalex.org/W2771965516","https://openalex.org/W2796779902","https://openalex.org/W2797184202","https://openalex.org/W2895748257","https://openalex.org/W2903549000","https://openalex.org/W2916798096","https://openalex.org/W2946127255","https://openalex.org/W2962729993","https://openalex.org/W2962896489","https://openalex.org/W2963402313","https://openalex.org/W2963598138","https://openalex.org/W2963781481","https://openalex.org/W2964221239","https://openalex.org/W2968510372","https://openalex.org/W2984313141","https://openalex.org/W3015042687","https://openalex.org/W3015197771","https://openalex.org/W3035068106","https://openalex.org/W3126761340","https://openalex.org/W4243493583","https://openalex.org/W6759363029","https://openalex.org/W6863994431","https://openalex.org/W6864014924"],"related_works":["https://openalex.org/W4251504644","https://openalex.org/W132764016","https://openalex.org/W2148054235","https://openalex.org/W2132043085","https://openalex.org/W2123437433","https://openalex.org/W2369285629","https://openalex.org/W3094179683","https://openalex.org/W2144496413","https://openalex.org/W2336873990","https://openalex.org/W2964084369"],"abstract_inverted_index":{"3-D":[0,16,34,49,80,121,149],"human":[1,10,19,50,73,83,118,150,177],"pose":[2,51,162],"estimation":[3,123,163],"is":[4,21,97,109],"a":[5,43,63,90],"crucial":[6],"step":[7],"for":[8,78],"understanding":[9],"actions.":[11],"However,":[12],"reliably":[13],"capturing":[14],"precise":[15],"position":[17],"of":[18,32,46,82,134,173],"joints":[20],"non-trivial":[22],"and":[23,71,117,164],"tedious.":[24],"Current":[25],"models":[26],"often":[27],"suffer":[28],"from":[29],"the":[30,65,69,114,143,171],"scarcity":[31],"high-quality":[33,148],"annotated":[35],"training":[36],"data.":[37],"In":[38,56],"this":[39,86,141],"work,":[40],"we":[41,88],"explore":[42],"novel":[44],"way":[45],"obtaining":[47],"gigantic":[48],"data":[52,151],"without":[53],"manual":[54],"annotations.":[55],"catedioptric":[57],"videos":[58],"(\\emph{e.g.},":[59],"people":[60],"dance":[61],"before":[62],"mirror),":[64],"camera":[66],"records":[67],"both":[68],"original":[70],"mirrored":[72],"poses,":[74],"which":[75,96],"provides":[76],"cues":[77],"estimating":[79],"positions":[81],"joints.":[84],"Following":[85],"idea,":[87],"crawl":[89],"large-scale":[91],"Dance-before-Mirror":[92],"(DBM)":[93],"video":[94],"dataset,":[95],"about":[98],"24":[99],"times":[100],"larger":[101],"than":[102],"existing":[103],"Human3.6M":[104],"benchmark.":[105],"Our":[106],"technical":[107],"insight":[108],"that,":[110],"by":[111],"jointly":[112],"harnessing":[113],"epipolar":[115],"geometry":[116],"skeleton":[119],"priors,":[120],"joint":[122],"can":[124],"boil":[125],"down":[126],"to":[127],"an":[128],"optimization":[129],"problem":[130],"over":[131],"two":[132],"sets":[133],"2-D":[135],"estimations.":[136],"To":[137],"our":[138,174],"best":[139],"knowledge,":[140],"represents":[142],"first":[144],"work":[145],"that":[146],"collects":[147],"via":[152],"catadioptric":[153],"systems.":[154],"We":[155],"have":[156],"conducted":[157],"comprehensive":[158],"experiments":[159],"on":[160],"cross-scenario":[161],"visualization":[165],"analysis.":[166],"The":[167],"results":[168],"strongly":[169],"demonstrate":[170],"usefulness":[172],"proposed":[175],"DBM":[176],"poses.":[178]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}