{"id":"https://openalex.org/W3067010945","doi":"https://doi.org/10.1145/3394171.3413955","title":"Unsupervised Learning Facial Parameter Regressor for Action Unit Intensity Estimation via Differentiable Renderer","display_name":"Unsupervised Learning Facial Parameter Regressor for Action Unit Intensity Estimation via Differentiable Renderer","publication_year":2020,"publication_date":"2020-10-12","ids":{"openalex":"https://openalex.org/W3067010945","doi":"https://doi.org/10.1145/3394171.3413955","mag":"3067010945"},"language":"en","primary_location":{"id":"doi:10.1145/3394171.3413955","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3394171.3413955","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2008.08862","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103079800","display_name":"Xinhui Song","orcid":"https://orcid.org/0000-0002-0082-9244"},"institutions":[{"id":"https://openalex.org/I4210091137","display_name":"NetEase (China)","ror":"https://ror.org/00fp6fj05","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210091137"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xinhui Song","raw_affiliation_strings":["Netease Fuxi AI Lab, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Netease Fuxi AI Lab, Hangzhou, China","institution_ids":["https://openalex.org/I4210091137"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042295331","display_name":"Tianyang Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210091137","display_name":"NetEase (China)","ror":"https://ror.org/00fp6fj05","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210091137"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianyang Shi","raw_affiliation_strings":["Netease Fuxi AI Lab, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Netease Fuxi AI Lab, Hangzhou, China","institution_ids":["https://openalex.org/I4210091137"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043914348","display_name":"Zunlei Feng","orcid":"https://orcid.org/0000-0001-8640-8434"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zunlei Feng","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026532752","display_name":"Mingli Song","orcid":"https://orcid.org/0000-0003-2621-6048"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingli Song","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021004721","display_name":"Jackie Lin","orcid":"https://orcid.org/0000-0003-3087-2857"},"institutions":[{"id":"https://openalex.org/I4210091137","display_name":"NetEase (China)","ror":"https://ror.org/00fp6fj05","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210091137"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jackie Lin","raw_affiliation_strings":["Netease Fuxi AI Lab, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Netease Fuxi AI Lab, Hangzhou, China","institution_ids":["https://openalex.org/I4210091137"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004770949","display_name":"Chuan\u2010Jie Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210091137","display_name":"NetEase (China)","ror":"https://ror.org/00fp6fj05","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210091137"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuanjie Lin","raw_affiliation_strings":["Netease Fuxi AI Lab, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Netease Fuxi AI Lab, Hangzhou, China","institution_ids":["https://openalex.org/I4210091137"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022008180","display_name":"Changjie Fan","orcid":"https://orcid.org/0000-0001-5420-0516"},"institutions":[{"id":"https://openalex.org/I4210091137","display_name":"NetEase (China)","ror":"https://ror.org/00fp6fj05","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210091137"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changjie Fan","raw_affiliation_strings":["Netease Fuxi AI Lab, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Netease Fuxi AI Lab, Hangzhou, China","institution_ids":["https://openalex.org/I4210091137"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100638623","display_name":"Yi Yuan","orcid":"https://orcid.org/0000-0003-2507-8181"},"institutions":[{"id":"https://openalex.org/I4210091137","display_name":"NetEase (China)","ror":"https://ror.org/00fp6fj05","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210091137"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Yuan","raw_affiliation_strings":["Netease Fuxi AI Lab, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Netease Fuxi AI Lab, Hangzhou, China","institution_ids":["https://openalex.org/I4210091137"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5103079800"],"corresponding_institution_ids":["https://openalex.org/I4210091137"],"apc_list":null,"apc_paid":null,"fwci":0.3929,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.62013171,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2842","last_page":"2851"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9980000257492065,"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/T11448","display_name":"Face recognition and analysis","score":0.9980000257492065,"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/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.9921000003814697,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T10667","display_name":"Emotion and Mood Recognition","score":0.9914000034332275,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7413491606712341},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6464220285415649},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5916439890861511},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.5745495557785034},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.5609705448150635},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45745885372161865},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.45658770203590393},{"id":"https://openalex.org/keywords/differentiable-function","display_name":"Differentiable function","score":0.4287441670894623},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.13520336151123047},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1338455080986023},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07254624366760254}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7413491606712341},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6464220285415649},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5916439890861511},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.5745495557785034},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.5609705448150635},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45745885372161865},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.45658770203590393},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.4287441670894623},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.13520336151123047},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1338455080986023},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07254624366760254},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3394171.3413955","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3394171.3413955","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Multimedia","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2008.08862","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2008.08862","pdf_url":"https://arxiv.org/pdf/2008.08862","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2008.08862","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2008.08862","pdf_url":"https://arxiv.org/pdf/2008.08862","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":65,"referenced_works":["https://openalex.org/W1616031638","https://openalex.org/W1628791547","https://openalex.org/W1661563386","https://openalex.org/W1686810756","https://openalex.org/W1896424170","https://openalex.org/W1900346672","https://openalex.org/W1935685005","https://openalex.org/W1964277727","https://openalex.org/W2017107803","https://openalex.org/W2045472600","https://openalex.org/W2051297709","https://openalex.org/W2062260304","https://openalex.org/W2083261637","https://openalex.org/W2086331119","https://openalex.org/W2091373806","https://openalex.org/W2098615198","https://openalex.org/W2103943262","https://openalex.org/W2107037917","https://openalex.org/W2141403362","https://openalex.org/W2142575466","https://openalex.org/W2145316937","https://openalex.org/W2161634108","https://openalex.org/W2173520492","https://openalex.org/W2194775991","https://openalex.org/W2237250383","https://openalex.org/W2272446670","https://openalex.org/W2398381847","https://openalex.org/W2430562337","https://openalex.org/W2469434562","https://openalex.org/W2470957930","https://openalex.org/W2502312327","https://openalex.org/W2519131448","https://openalex.org/W2558246262","https://openalex.org/W2584229793","https://openalex.org/W2588367409","https://openalex.org/W2604672468","https://openalex.org/W2606784655","https://openalex.org/W2621925205","https://openalex.org/W2738134571","https://openalex.org/W2752782242","https://openalex.org/W2756460808","https://openalex.org/W2767348466","https://openalex.org/W2771328060","https://openalex.org/W2798291180","https://openalex.org/W2798764454","https://openalex.org/W2799151537","https://openalex.org/W2886934227","https://openalex.org/W2912817214","https://openalex.org/W2917887692","https://openalex.org/W2945729334","https://openalex.org/W2949092679","https://openalex.org/W2949117887","https://openalex.org/W2953853258","https://openalex.org/W2962770929","https://openalex.org/W2962898354","https://openalex.org/W2963420686","https://openalex.org/W2963460857","https://openalex.org/W2963713173","https://openalex.org/W2964014798","https://openalex.org/W2964322530","https://openalex.org/W2981595954","https://openalex.org/W2981915309","https://openalex.org/W3015493566","https://openalex.org/W4285719527","https://openalex.org/W6600195515"],"related_works":["https://openalex.org/W4285277090","https://openalex.org/W4327738859","https://openalex.org/W2348722996","https://openalex.org/W2334570605","https://openalex.org/W3181683615","https://openalex.org/W4286826125","https://openalex.org/W1633485514","https://openalex.org/W4287880334","https://openalex.org/W1604739066","https://openalex.org/W2115878407"],"abstract_inverted_index":{"Facial":[0],"action":[1],"unit":[2],"(AU)":[3],"intensity":[4,19],"is":[5],"an":[6],"index":[7],"to":[8,42,105],"describe":[9],"all":[10],"visually":[11],"discernible":[12],"facial":[13,45,75,103],"movements.":[14],"Most":[15],"existing":[16],"methods":[17],"learn":[18],"estimator":[20],"with":[21,94],"limited":[22],"AU":[23,51],"data,":[24],"while":[25],"they":[26],"lack":[27],"of":[28,32,67,86,97,162],"generalization":[29],"ability":[30],"out":[31],"the":[33,44,82,87,95,98,102,106,123,140,150,155,160,166],"dataset.":[34],"In":[35],"this":[36],"paper,":[37],"we":[38],"present":[39],"a":[40,55,68,71,74,90,110],"framework":[41,65],"predict":[43],"parameters":[46,49,85,104],"(including":[47],"identity":[48,114],"and":[50,73,118,135],"parameters)":[52],"based":[53],"on":[54,130],"bone-driven":[56],"face":[57,92],"model":[58],"(BDFM)":[59],"under":[60],"different":[61],"views.":[62],"The":[63,78],"proposed":[64,141],"consists":[66],"feature":[69],"extractor,":[70],"generator,":[72,99],"parameter":[76],"regressor.":[77],"regressor":[79],"can":[80,121,143],"fit":[81],"physical":[83],"meaning":[84],"BDFM":[88],"from":[89],"single":[91],"image":[93],"help":[96],"which":[100,137],"maps":[101],"game-face":[107],"images":[108],"as":[109],"differentiable":[111],"renderer.":[112],"Besides,":[113],"loss,":[115,117],"loopback":[116],"adversarial":[119],"loss":[120],"improve":[122],"regressive":[124],"results.":[125],"Quantitative":[126],"evaluations":[127],"are":[128],"performed":[129],"two":[131],"public":[132],"databases":[133],"BP4D":[134],"DISFA,":[136],"demonstrates":[138],"that":[139],"method":[142,164],"achieve":[144],"comparable":[145],"or":[146],"better":[147],"performance":[148],"than":[149],"state-of-the-art":[151],"methods.":[152],"What's":[153],"more,":[154],"qualitative":[156],"results":[157],"also":[158],"demonstrate":[159],"validity":[161],"our":[163],"in":[165],"wild.":[167]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2020-08-24T00:00:00"}
