{"id":"https://openalex.org/W4415318670","doi":"https://doi.org/10.1109/vcip67698.2025.11396803","title":"Efficient Label Refinement for Face Parsing Under Extreme Poses Using 3D Gaussian Splatting","display_name":"Efficient Label Refinement for Face Parsing Under Extreme Poses Using 3D Gaussian Splatting","publication_year":2025,"publication_date":"2025-12-01","ids":{"openalex":"https://openalex.org/W4415318670","doi":"https://doi.org/10.1109/vcip67698.2025.11396803"},"language":"en","primary_location":{"id":"doi:10.1109/vcip67698.2025.11396803","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip67698.2025.11396803","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2510.08096","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120050644","display_name":"Ankit Gahlawat","orcid":null},"institutions":[{"id":"https://openalex.org/I181514455","display_name":"International Institute of Information Technology Bangalore","ror":"https://ror.org/05h9eqy10","country_code":"IN","type":"education","lineage":["https://openalex.org/I181514455"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ankit Gahlawat","raw_affiliation_strings":["Bangalore (IIIT-B),International Institute of Information Technology,Bengaluru,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bangalore (IIIT-B),International Institute of Information Technology,Bengaluru,India","institution_ids":["https://openalex.org/I181514455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101655597","display_name":"Anirban Mukherjee","orcid":"https://orcid.org/0000-0003-3404-7045"},"institutions":[{"id":"https://openalex.org/I181514455","display_name":"International Institute of Information Technology Bangalore","ror":"https://ror.org/05h9eqy10","country_code":"IN","type":"education","lineage":["https://openalex.org/I181514455"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Anirban Mukherjee","raw_affiliation_strings":["Bangalore (IIIT-B),International Institute of Information Technology,Bengaluru,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bangalore (IIIT-B),International Institute of Information Technology,Bengaluru,India","institution_ids":["https://openalex.org/I181514455"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066864733","display_name":"Dinesh Babu Jayagopi","orcid":"https://orcid.org/0000-0003-0080-452X"},"institutions":[{"id":"https://openalex.org/I181514455","display_name":"International Institute of Information Technology Bangalore","ror":"https://ror.org/05h9eqy10","country_code":"IN","type":"education","lineage":["https://openalex.org/I181514455"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Dinesh Babu Jayagopi","raw_affiliation_strings":["Bangalore (IIIT-B),International Institute of Information Technology,Bengaluru,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bangalore (IIIT-B),International Institute of Information Technology,Bengaluru,India","institution_ids":["https://openalex.org/I181514455"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I181514455"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31273234,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.996399998664856,"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/T10057","display_name":"Face and Expression Recognition","score":0.996399998664856,"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/T11448","display_name":"Face recognition and analysis","score":0.9957000017166138,"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":0.987500011920929,"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/parsing","display_name":"Parsing","score":0.6972000002861023},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5899999737739563},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.559499979019165},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5235999822616577},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.5095000267028809},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.44769999384880066},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.43709999322891235},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4059000015258789}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8841999769210815},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.6972000002861023},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6628000140190125},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5899999737739563},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.559499979019165},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5426999926567078},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5235999822616577},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.5095000267028809},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.44769999384880066},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.43709999322891235},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4059000015258789},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.3774999976158142},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3772999942302704},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.37689998745918274},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.37540000677108765},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.36550000309944153},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35910001397132874},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3560999929904938},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3529999852180481},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.32179999351501465},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.3070000112056732}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/vcip67698.2025.11396803","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip67698.2025.11396803","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2510.08096","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.08096","pdf_url":"https://arxiv.org/pdf/2510.08096","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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"},{"id":"doi:10.48550/arxiv.2510.08096","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.08096","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2510.08096","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.08096","pdf_url":"https://arxiv.org/pdf/2510.08096","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415318670.pdf","grobid_xml":"https://content.openalex.org/works/W4415318670.grobid-xml"},"referenced_works_count":13,"referenced_works":["https://openalex.org/W2165558283","https://openalex.org/W4385318467","https://openalex.org/W3014006537","https://openalex.org/W4372283500","https://openalex.org/W4408111637","https://openalex.org/W1796263212","https://openalex.org/W3034521057","https://openalex.org/W3159093935","https://openalex.org/W2998428583","https://openalex.org/W3211490618","https://openalex.org/W3034798648","https://openalex.org/W2886934227","https://openalex.org/W4386065603"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"face":[1,88,150],"parsing":[2,89,151],"under":[3],"extreme":[4],"viewing":[5],"angles":[6],"remains":[7],"a":[8,30,87,134,143],"significant":[9],"challenge":[10],"due":[11],"to":[12,41,57,62,122],"limited":[13],"labeled":[14],"data":[15,81],"in":[16,153],"such":[17],"poses.":[18],"Manual":[19],"annotation":[20],"is":[21],"costly":[22],"and":[23,60,131,145],"often":[24],"impractical":[25],"at":[26],"scale.":[27],"We":[28],"propose":[29],"novel":[31],"label":[32],"refinement":[33],"pipeline":[34],"that":[35,115],"leverages":[36],"3D":[37,129],"Gaussian":[38],"Splatting":[39],"(3DGS)":[40],"generate":[42],"accurate":[43],"segmentation":[44,65],"masks":[45],"from":[46],"noisy":[47],"multiview":[48,70],"predictions.":[49],"By":[50],"jointly":[51],"fitting":[52],"two":[53],"3DGS":[54],"models,":[55],"one":[56,61],"RGB":[58],"images":[59],"their":[63],"initial":[64,138],"maps,":[66],"our":[67,116],"method":[68,141],"enforces":[69],"consistency":[71],"through":[72],"shared":[73],"geometry,":[74],"enabling":[75],"the":[76],"synthesis":[77],"of":[78,137],"pose-diverse":[79],"training":[80],"with":[82],"only":[83,133],"minimal":[84],"post-processing.":[85],"Fine-tuning":[86],"model":[90],"on":[91,98,106],"this":[92],"refined":[93],"dataset":[94],"significantly":[95],"improves":[96],"accuracy":[97],"challenging":[99],"head":[100],"poses,":[101],"while":[102],"maintaining":[103],"strong":[104],"performance":[105],"standard":[107],"views.":[108],"Extensive":[109],"experiments,":[110],"including":[111],"human":[112],"evaluations,":[113],"demonstrate":[114],"approach":[117],"achieves":[118],"superior":[119],"results":[120],"compared":[121],"state-of-the-art":[123],"methods,":[124],"despite":[125],"requiring":[126],"no":[127],"ground-truth":[128],"annotations":[130],"using":[132],"small":[135],"set":[136],"images.":[139],"Our":[140],"offers":[142],"scalable":[144],"effective":[146],"solution":[147],"for":[148],"improving":[149],"robustness":[152],"real-world":[154],"settings.":[155]},"counts_by_year":[],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-18T00:00:00"}