{"id":"https://openalex.org/W3009175371","doi":"https://doi.org/10.1109/wacv45572.2020.9093380","title":"DGGAN: Depth-image Guided Generative Adversarial Networks for Disentangling RGB and Depth Images in 3D Hand Pose Estimation","display_name":"DGGAN: Depth-image Guided Generative Adversarial Networks for Disentangling RGB and Depth Images in 3D Hand Pose Estimation","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3009175371","doi":"https://doi.org/10.1109/wacv45572.2020.9093380","mag":"3009175371"},"language":"en","primary_location":{"id":"doi:10.1109/wacv45572.2020.9093380","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093380","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","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/A5049251280","display_name":"Liangjian Chen","orcid":"https://orcid.org/0000-0001-7038-9144"},"institutions":[{"id":"https://openalex.org/I204250578","display_name":"University of California, Irvine","ror":"https://ror.org/04gyf1771","country_code":"US","type":"education","lineage":["https://openalex.org/I204250578"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Liangjian Chen","raw_affiliation_strings":["University of California, Irvine"],"affiliations":[{"raw_affiliation_string":"University of California, Irvine","institution_ids":["https://openalex.org/I204250578"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059470173","display_name":"Shih-Yao Lin","orcid":"https://orcid.org/0000-0003-3160-669X"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shih-Yao Lin","raw_affiliation_strings":["Tencent America"],"affiliations":[{"raw_affiliation_string":"Tencent America","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058457237","display_name":"Yusheng Xie","orcid":"https://orcid.org/0000-0002-8581-4614"},"institutions":[{"id":"https://openalex.org/I4210089985","display_name":"Amazon (Germany)","ror":"https://ror.org/00b9ktm87","country_code":"DE","type":"company","lineage":["https://openalex.org/I1311688040","https://openalex.org/I4210089985"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Yusheng Xie","raw_affiliation_strings":["Amazon"],"affiliations":[{"raw_affiliation_string":"Amazon","institution_ids":["https://openalex.org/I4210089985"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002217153","display_name":"Yen\u2010Yu Lin","orcid":"https://orcid.org/0000-0002-7183-6070"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yen-Yu Lin","raw_affiliation_strings":["National Chiao Tung University"],"affiliations":[{"raw_affiliation_string":"National Chiao Tung University","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100380499","display_name":"Wei Fan","orcid":"https://orcid.org/0000-0002-0342-6272"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Fan","raw_affiliation_strings":["Tencent America"],"affiliations":[{"raw_affiliation_string":"Tencent America","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084618257","display_name":"Xiaohui Xie","orcid":"https://orcid.org/0000-0002-5479-6345"},"institutions":[{"id":"https://openalex.org/I204250578","display_name":"University of California, Irvine","ror":"https://ror.org/04gyf1771","country_code":"US","type":"education","lineage":["https://openalex.org/I204250578"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaohui Xie","raw_affiliation_strings":["University of California, Irvine"],"affiliations":[{"raw_affiliation_string":"University of California, Irvine","institution_ids":["https://openalex.org/I204250578"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5049251280"],"corresponding_institution_ids":["https://openalex.org/I204250578"],"apc_list":null,"apc_paid":null,"fwci":3.224,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.93325267,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"400","last_page":"408"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9975000023841858,"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":0.9975000023841858,"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":0.9628000259399414,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.9505000114440918,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7672934532165527},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.7569797039031982},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6653684973716736},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6639045476913452},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.6523361206054688},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6384833455085754},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6344769597053528},{"id":"https://openalex.org/keywords/depth-map","display_name":"Depth map","score":0.5880010724067688},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5553104281425476},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.534791886806488},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.49113914370536804},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4869004786014557},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.4629906415939331},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3990258276462555},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2325180470943451},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07893398404121399},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07152816653251648}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7672934532165527},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.7569797039031982},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6653684973716736},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6639045476913452},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.6523361206054688},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6384833455085754},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6344769597053528},{"id":"https://openalex.org/C141268832","wikidata":"https://www.wikidata.org/wiki/Q2940499","display_name":"Depth map","level":3,"score":0.5880010724067688},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5553104281425476},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.534791886806488},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.49113914370536804},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4869004786014557},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.4629906415939331},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3990258276462555},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2325180470943451},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07893398404121399},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07152816653251648},{"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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv45572.2020.9093380","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093380","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.5299999713897705}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W1928739709","https://openalex.org/W1969724184","https://openalex.org/W1990947293","https://openalex.org/W2017817327","https://openalex.org/W2099471712","https://openalex.org/W2402846924","https://openalex.org/W2480085607","https://openalex.org/W2546353648","https://openalex.org/W2559085405","https://openalex.org/W2560609797","https://openalex.org/W2605462541","https://openalex.org/W2734748860","https://openalex.org/W2781475793","https://openalex.org/W2796453247","https://openalex.org/W2798581336","https://openalex.org/W2799191197","https://openalex.org/W2800786295","https://openalex.org/W2892644985","https://openalex.org/W2894835560","https://openalex.org/W2896229066","https://openalex.org/W2897765997","https://openalex.org/W2903283814","https://openalex.org/W2920350521","https://openalex.org/W2946127255","https://openalex.org/W2950111065","https://openalex.org/W2950642167","https://openalex.org/W2962793481","https://openalex.org/W2962808524","https://openalex.org/W2962811204","https://openalex.org/W2962926199","https://openalex.org/W2963073614","https://openalex.org/W2963207848","https://openalex.org/W2963234092","https://openalex.org/W2963515833","https://openalex.org/W2963601560","https://openalex.org/W2963950354","https://openalex.org/W2964093990","https://openalex.org/W2964211001","https://openalex.org/W2964304707","https://openalex.org/W2979577579","https://openalex.org/W2982601397","https://openalex.org/W3023468226","https://openalex.org/W4300106150","https://openalex.org/W4320013936","https://openalex.org/W6682642761","https://openalex.org/W6729320488","https://openalex.org/W6730277886","https://openalex.org/W6736205797","https://openalex.org/W6746282794","https://openalex.org/W6750700183","https://openalex.org/W6756070714","https://openalex.org/W6756495640","https://openalex.org/W6762390211","https://openalex.org/W6763422710","https://openalex.org/W6777048179"],"related_works":["https://openalex.org/W2353179089","https://openalex.org/W2923538289","https://openalex.org/W2353125546","https://openalex.org/W2470643824","https://openalex.org/W2349635380","https://openalex.org/W3102673927","https://openalex.org/W2327954668","https://openalex.org/W4294967731","https://openalex.org/W2022566595","https://openalex.org/W3202440119"],"abstract_inverted_index":{"Estimating":[0],"3D":[1,39,53,112,160],"hand":[2,40,113],"poses":[3,54],"from":[4,29],"RGB":[5,30,66,101],"images":[6,67],"is":[7,17],"essential":[8],"to":[9,20,46,92,109],"a":[10,80],"wide":[11],"range":[12],"of":[13,26],"potential":[14],"applications,":[15],"but":[16],"challenging":[18],"owing":[19],"substantial":[21],"ambiguity":[22],"in":[23,143,153],"the":[24,48,51,56,69,99,105,111,119,133,145,158,170],"inference":[25],"depth":[27,58,71,95,107,123,135],"information":[28],"images.":[31],"State-of-the-art":[32],"estimators":[33,62],"address":[34],"this":[35,76],"problem":[36],"by":[37,138,164],"regularizing":[38,144],"pose":[41,114,146],"estimation":[42,115,147,154],"models":[43],"during":[44,73],"training":[45],"enforce":[47],"consistency":[49],"between":[50],"predicted":[52],"and":[55,68,103,167,173],"ground-truth":[57,122],"maps.":[59,124],"However,":[60],"these":[61],"rely":[63],"on":[64,98,127,169],"both":[65],"paired":[70],"maps":[72,96,108,136],"training.":[74],"In":[75],"study,":[77],"we":[78],"propose":[79],"conditional":[81],"generative":[82],"adversarial":[83],"network":[84],"(GAN)":[85],"model,":[86,116,148],"called":[87],"Depth-image":[88],"Guided":[89],"GAN":[90],"(DGGAN),":[91],"generate":[93],"realistic":[94],"conditioned":[97],"input":[100],"image,":[102],"use":[104],"synthesized":[106,134],"regularize":[110],"therefore":[117],"eliminating":[118],"need":[120],"for":[121],"Experimental":[125],"results":[126,152],"multiple":[128],"benchmark":[129],"datasets":[130],"show":[131],"that":[132],"produced":[137],"DGGAN":[139],"are":[140],"quite":[141],"effective":[142],"yielding":[149],"new":[150],"state-of-the-art":[151],"accuracy,":[155],"notably":[156],"reducing":[157],"mean":[159],"endpoint":[161],"errors":[162],"(EPE)":[163],"4.7%,":[165],"16.5%,":[166],"6.8%":[168],"RHD,":[171],"STB":[172],"MHP":[174],"datasets,":[175],"respectively.":[176]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":7}],"updated_date":"2026-01-11T23:08:45.486102","created_date":"2025-10-10T00:00:00"}
