{"id":"https://openalex.org/W2912365016","doi":"https://doi.org/10.2312/stag.20181303","title":"An Optimized Marker Layout for 3D Facial Motion Capture","display_name":"An Optimized Marker Layout for 3D Facial Motion Capture","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2912365016","doi":"https://doi.org/10.2312/stag.20181303","mag":"2912365016"},"language":"en","primary_location":{"id":"doi:10.2312/stag.20181303","is_oa":true,"landing_page_url":"https://doi.org/10.2312/stag.20181303","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.2312/stag.20181303","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075031328","display_name":"Ackley Dias Will","orcid":"https://orcid.org/0000-0001-7828-1007"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Will, A. D.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005663220","display_name":"Jos\u00e9 Mario De Martino","orcid":"https://orcid.org/0000-0002-9707-799X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Martino, J. M. De","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5088755807","display_name":"Juliana dos Santos Bezerra","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bezerra, J.","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5075031328"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1045,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.48753538,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9959999918937683,"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":0.9959999918937683,"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/motion-capture","display_name":"Motion capture","score":0.58933025598526},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5562166571617126},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5413527488708496},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5357932448387146},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.4333766996860504}],"concepts":[{"id":"https://openalex.org/C48007421","wikidata":"https://www.wikidata.org/wiki/Q676252","display_name":"Motion capture","level":3,"score":0.58933025598526},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5562166571617126},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5413527488708496},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5357932448387146},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.4333766996860504}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.2312/stag.20181303","is_oa":true,"landing_page_url":"https://doi.org/10.2312/stag.20181303","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"doi:10.2312/stag.20181303","is_oa":true,"landing_page_url":"https://doi.org/10.2312/stag.20181303","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2903189430","https://openalex.org/W2390186842","https://openalex.org/W2807843367","https://openalex.org/W2323174668","https://openalex.org/W3198851718","https://openalex.org/W3098830135","https://openalex.org/W2910182126","https://openalex.org/W2603616215","https://openalex.org/W2923719454","https://openalex.org/W2358721113","https://openalex.org/W2358900587","https://openalex.org/W3012876523","https://openalex.org/W3169129874","https://openalex.org/W2383717232","https://openalex.org/W1547111151","https://openalex.org/W2997065218","https://openalex.org/W2748502319","https://openalex.org/W3127548867","https://openalex.org/W2117052358","https://openalex.org/W1516532448"],"abstract_inverted_index":{"Facial":[0,24,53],"motion":[1],"capture":[2,28],"(Facial":[3],"Mocap)":[4],"has":[5],"gained":[6],"increasing":[7],"attention":[8],"from":[9,13,35],"researchers":[10],"and":[11,22,62,120,124,152],"professionals":[12],"different":[14],"areas":[15],"of":[16,29,32,48,65,86,93,102,136],"interest,":[17],"including":[18],"entertainment,":[19],"face-to-face":[20],"communication,":[21],"training.":[23],"Mocap":[25,54],"allows":[26],"straightforward":[27],"dynamic":[30],"behavior":[31],"the":[33,60,66,84,91,99,103,118,129,134,154],"face":[34,67,85],"live":[36],"action,":[37],"providing":[38],"data":[39],"that":[40],"can":[41],"be":[42],"used":[43],"to":[44,59,116],"drive":[45],"realistic":[46],"animation":[47],"a":[49,139],"3D":[50],"virtual":[51],"face.":[52],"is":[55,105],"an":[56,87,107,113,145],"advantageous":[57],"alternative":[58],"direct":[61],"laborious":[63],"manipulation":[64],"model.":[68],"In":[69],"particular,":[70],"marker-based":[71],"mocap":[72],"technique":[73,115,143],"acquires":[74],"three-dimensional":[75],"facial":[76,96,122],"points":[77],"trajectories":[78],"by":[79],"tracking":[80],"markers":[81,104,123,137,151],"fixed":[82],"on":[83,128,133],"actor.":[88],"However,":[89],"despite":[90],"existence":[92],"several":[94],"empirical":[95],"marker":[97,147],"layouts,":[98],"ideal":[100],"positioning":[101,121,153],"still":[106],"open":[108],"question.":[109],"This":[110],"paper":[111],"presents":[112],"optimization":[114],"calculate":[117],"quantity":[119],"establish":[125],"their":[126],"influences":[127],"polygon":[130],"mesh":[131],"based":[132],"correlation":[135],"in":[138],"dense":[140],"layout.":[141],"The":[142],"generates":[144],"optimized":[146],"layout":[148],"discarding":[149],"unnecessary":[150],"remaining":[155],"ones.":[156]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}