{"id":"https://openalex.org/W4389890998","doi":"https://doi.org/10.1109/access.2023.3344658","title":"3DHR-Co: A Collaborative Test-Time Refinement Framework for In-the-Wild 3D Human-Body Reconstruction Task","display_name":"3DHR-Co: A Collaborative Test-Time Refinement Framework for In-the-Wild 3D Human-Body Reconstruction Task","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4389890998","doi":"https://doi.org/10.1109/access.2023.3344658"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3344658","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1109/access.2023.3344658","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10365137.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10365137.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5048066371","display_name":"Jonathan Samuel Lumentut","orcid":"https://orcid.org/0000-0001-5146-8648"},"institutions":[{"id":"https://openalex.org/I166073570","display_name":"Binus University","ror":"https://ror.org/03zmf4s77","country_code":"ID","type":"education","lineage":["https://openalex.org/I166073570"]},{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["ID","KR"],"is_corresponding":true,"raw_author_name":"Jonathan Samuel Lumentut","raw_affiliation_strings":["Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, South Korea","School of Computer Science, Bina Nusantara University, Jakarta, Indonesia"],"affiliations":[{"raw_affiliation_string":"Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]},{"raw_affiliation_string":"School of Computer Science, Bina Nusantara University, Jakarta, Indonesia","institution_ids":["https://openalex.org/I166073570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046504049","display_name":"Kyoung Mu Lee","orcid":"https://orcid.org/0000-0001-7210-1036"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kyoung Mu Lee","raw_affiliation_strings":["Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, South Korea","LG-SNU AI Research, Seoul National University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]},{"raw_affiliation_string":"LG-SNU AI Research, Seoul National University, Seoul, Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5048066371"],"corresponding_institution_ids":["https://openalex.org/I139264467","https://openalex.org/I166073570"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17320768,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"11","issue":null,"first_page":"145254","last_page":"145263"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9991999864578247,"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":0.9991999864578247,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9937999844551086,"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/overfitting","display_name":"Overfitting","score":0.8883707523345947},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.833432674407959},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7316766977310181},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6152499318122864},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5659100413322449},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5445412397384644},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.5384549498558044},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5320481061935425},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.47030967473983765},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.432561457157135}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8883707523345947},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.833432674407959},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7316766977310181},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6152499318122864},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5659100413322449},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5445412397384644},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.5384549498558044},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5320481061935425},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.47030967473983765},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.432561457157135},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3344658","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1109/access.2023.3344658","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10365137.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:11105af3148f451c910e768e6ca5f518","is_oa":true,"landing_page_url":"https://doaj.org/article/11105af3148f451c910e768e6ca5f518","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 145254-145263 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3344658","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1109/access.2023.3344658","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10365137.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4389890998.pdf","grobid_xml":"https://content.openalex.org/works/W4389890998.grobid-xml"},"referenced_works_count":45,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1967554269","https://openalex.org/W2080873731","https://openalex.org/W2101032778","https://openalex.org/W2194775991","https://openalex.org/W2768683308","https://openalex.org/W2888934629","https://openalex.org/W2895748257","https://openalex.org/W2962954622","https://openalex.org/W2963475767","https://openalex.org/W2963515833","https://openalex.org/W2963704386","https://openalex.org/W2963781481","https://openalex.org/W2963995996","https://openalex.org/W2964179555","https://openalex.org/W2968466051","https://openalex.org/W2970285700","https://openalex.org/W2975420824","https://openalex.org/W2978956737","https://openalex.org/W2981637078","https://openalex.org/W2981954551","https://openalex.org/W2981978060","https://openalex.org/W3034482680","https://openalex.org/W3035291735","https://openalex.org/W3035304632","https://openalex.org/W3035551320","https://openalex.org/W3035581100","https://openalex.org/W3106892578","https://openalex.org/W3107167007","https://openalex.org/W3107505690","https://openalex.org/W3109877674","https://openalex.org/W3110022498","https://openalex.org/W3118973820","https://openalex.org/W3128923825","https://openalex.org/W3174980830","https://openalex.org/W3175852502","https://openalex.org/W3177949351","https://openalex.org/W3183434010","https://openalex.org/W3202432020","https://openalex.org/W3213343976","https://openalex.org/W4214770715","https://openalex.org/W4221142859","https://openalex.org/W4312799843","https://openalex.org/W4313175219","https://openalex.org/W6780178746"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W4376166922","https://openalex.org/W4378510483","https://openalex.org/W2745033168"],"abstract_inverted_index":{"The":[0,182],"task":[1,74,112],"of":[2,25,85,202,236,260],"3D":[3,44,55,86],"human-body":[4],"reconstruction":[5],"(3DHR),":[6],"which":[7],"mostly":[8],"utilizes":[9],"parametric":[10],"pose":[11,57,211],"and":[12,189],"shape":[13],"representations,":[14],"has":[15],"witnessed":[16],"significant":[17],"advances":[18],"in":[19,28,40,147,264],"recent":[20],"years.":[21],"However,":[22,91],"the":[23,37,53,83,108,164,173,179,191,194,200,217,221,233,237,252,258,265],"application":[24],"3DHR":[26,38,73,105,109,125,145,180,205,240,266],"techniques":[27],"handling":[29],"real-world":[30],"in-the-wild":[31,60,89,222],"data,":[32],"still":[33],"faces":[34],"limitations.":[35],"Training":[36],"model":[39],"such":[41],"scenario":[42],"with":[43,163],"human":[45,56,78],"pose\u2019s":[46],"ground":[47],"truth":[48],"(GT)":[49],"is":[50,159,185],"non-trivial.":[51],"Curating":[52],"accurate":[54],"GT":[58],"for":[59],"scenes":[61],"remains":[62],"difficult":[63],"due":[64],"to":[65,81,151,176,208,255],"various":[66,144,249],"factors.":[67],"Recent":[68],"test-time":[69,110,126,165],"refinement":[70,111,127],"approaches":[71],"on":[72,88,103,190,220,243,251],"leverage":[75],"2D":[76,96],"off-the-shelf":[77],"keypoints":[79],"information":[80],"support":[82],"lack":[84],"supervision":[87,97],"data.":[90,224],"we":[92,134,246],"observed":[93],"that":[94,123,140,171,228],"additional":[95],"alone":[98],"could":[99],"cause":[100],"overfitting":[101,174],"issue":[102,175],"common":[104,203,238],"backbones,":[106],"making":[107],"seem":[113],"intractable.":[114],"We":[115],"answer":[116],"this":[117],"challenge":[118],"by":[119,142],"proposing":[120],"a":[121,130,137,148],"strategy":[122,263],"complements":[124],"work":[128,167,196],"under":[129,168],"collaborative":[131,262],"approach.":[132],"Specifically,":[133],"initially":[135],"apply":[136],"pre-adaptation":[138],"approach":[139,158,230],"works":[141],"collaborating":[143],"models":[146],"single":[149],"framework":[150,184,254],"directly":[152],"improve":[153],"their":[154],"initial":[155],"outputs.":[156],"This":[157],"then":[160],"further":[161,177,247],"combined":[162],"adaptation":[166],"specific":[169],"settings":[170,250],"minimize":[172],"boost":[178],"performance.":[181],"whole":[183],"termed":[186],"as":[187],"3DHR-Co,":[188],"experiment":[192],"side,":[193],"proposed":[195,253],"can":[197],"significantly":[198],"enhance":[199],"scores":[201],"classic":[204,239],"backbones":[206],"up":[207],"-34":[209],"mm":[210],"error":[212],"suppression,":[213],"putting":[214],"them":[215],"among":[216],"top":[218],"list":[219],"benchmark":[223],"Such":[225],"achievement":[226],"shows":[227],"our":[229,261],"helps":[231],"unveil":[232],"true":[234],"potential":[235],"backbones.":[241],"Based":[242],"these":[244],"findings,":[245],"investigate":[248],"better":[256],"elaborate":[257],"capability":[259],"task.":[267]},"counts_by_year":[],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}
