Large Scale 3D Morphable Models
We present large scale facial model (LSFM)—a 3D Morphable Model (3DMM) automatically constructed from 9663 distinct facial identities. To the best of our knowledge LSFM is the largest-scale Morphable Model ever constructed, containing statistical information from a huge variety of the human populati...
Uloženo v:
| Vydáno v: | International journal of computer vision Ročník 126; číslo 2-4; s. 233 - 254 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
Springer US
01.04.2018
Springer Springer Nature B.V |
| Témata: | |
| ISSN: | 0920-5691, 1573-1405 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | We present large scale facial model (LSFM)—a 3D Morphable Model (3DMM) automatically constructed from 9663 distinct facial identities. To the best of our knowledge LSFM is the largest-scale Morphable Model ever constructed, containing statistical information from a huge variety of the human population. To build such a large model we introduce a novel fully automated and robust Morphable Model construction pipeline, informed by an evaluation of state-of-the-art dense correspondence techniques. The dataset that LSFM is trained on includes rich demographic information about each subject, allowing for the construction of not only a global 3DMM model but also models tailored for specific age, gender or ethnicity groups. We utilize the proposed model to perform age classification from 3D shape alone and to reconstruct noisy out-of-sample data in the low-dimensional model space. Furthermore, we perform a systematic analysis of the constructed 3DMM models that showcases their quality and descriptive power. The presented extensive qualitative and quantitative evaluations reveal that the proposed 3DMM achieves state-of-the-art results, outperforming existing models by a large margin. Finally, for the benefit of the research community, we make publicly available the source code of the proposed automatic 3DMM construction pipeline, as well as the constructed global 3DMM and a variety of bespoke models tailored by age, gender and ethnicity. |
|---|---|
| AbstractList | We present large scale facial model (LSFM)-a 3D Morphable Model (3DMM) automatically constructed from 9663 distinct facial identities. To the best of our knowledge LSFM is the largest-scale Morphable Model ever constructed, containing statistical information from a huge variety of the human population. To build such a large model we introduce a novel fully automated and robust Morphable Model construction pipeline, informed by an evaluation of state-of-the-art dense correspondence techniques. The dataset that LSFM is trained on includes rich demographic information about each subject, allowing for the construction of not only a global 3DMM model but also models tailored for specific age, gender or ethnicity groups. We utilize the proposed model to perform age classification from 3D shape alone and to reconstruct noisy out-of-sample data in the low-dimensional model space. Furthermore, we perform a systematic analysis of the constructed 3DMM models that showcases their quality and descriptive power. The presented extensive qualitative and quantitative evaluations reveal that the proposed 3DMM achieves state-of-the-art results, outperforming existing models by a large margin. Finally, for the benefit of the research community, we make publicly available the source code of the proposed automatic 3DMM construction pipeline, as well as the constructed global 3DMM and a variety of bespoke models tailored by age, gender and ethnicity.We present large scale facial model (LSFM)-a 3D Morphable Model (3DMM) automatically constructed from 9663 distinct facial identities. To the best of our knowledge LSFM is the largest-scale Morphable Model ever constructed, containing statistical information from a huge variety of the human population. To build such a large model we introduce a novel fully automated and robust Morphable Model construction pipeline, informed by an evaluation of state-of-the-art dense correspondence techniques. The dataset that LSFM is trained on includes rich demographic information about each subject, allowing for the construction of not only a global 3DMM model but also models tailored for specific age, gender or ethnicity groups. We utilize the proposed model to perform age classification from 3D shape alone and to reconstruct noisy out-of-sample data in the low-dimensional model space. Furthermore, we perform a systematic analysis of the constructed 3DMM models that showcases their quality and descriptive power. The presented extensive qualitative and quantitative evaluations reveal that the proposed 3DMM achieves state-of-the-art results, outperforming existing models by a large margin. Finally, for the benefit of the research community, we make publicly available the source code of the proposed automatic 3DMM construction pipeline, as well as the constructed global 3DMM and a variety of bespoke models tailored by age, gender and ethnicity. We present large scale facial model (LSFM)—a 3D Morphable Model (3DMM) automatically constructed from 9663 distinct facial identities. To the best of our knowledge LSFM is the largest-scale Morphable Model ever constructed, containing statistical information from a huge variety of the human population. To build such a large model we introduce a novel fully automated and robust Morphable Model construction pipeline, informed by an evaluation of state-of-the-art dense correspondence techniques. The dataset that LSFM is trained on includes rich demographic information about each subject, allowing for the construction of not only a global 3DMM model but also models tailored for specific age, gender or ethnicity groups. We utilize the proposed model to perform age classification from 3D shape alone and to reconstruct noisy out-of-sample data in the low-dimensional model space. Furthermore, we perform a systematic analysis of the constructed 3DMM models that showcases their quality and descriptive power. The presented extensive qualitative and quantitative evaluations reveal that the proposed 3DMM achieves state-of-the-art results, outperforming existing models by a large margin. Finally, for the benefit of the research community, we make publicly available the source code of the proposed automatic 3DMM construction pipeline, as well as the constructed global 3DMM and a variety of bespoke models tailored by age, gender and ethnicity. |
| Audience | Academic |
| Author | Roussos, Anastasios Booth, James Dunaway, David Ponniah, Allan Zafeiriou, Stefanos |
| Author_xml | – sequence: 1 givenname: James orcidid: 0000-0003-2114-9595 surname: Booth fullname: Booth, James email: james.booth@imperial.ac.uk organization: Imperial College London – sequence: 2 givenname: Anastasios surname: Roussos fullname: Roussos, Anastasios organization: Imperial College London, University of Exeter – sequence: 3 givenname: Allan surname: Ponniah fullname: Ponniah, Allan organization: Great Ormond Street Hospital – sequence: 4 givenname: David surname: Dunaway fullname: Dunaway, David organization: Great Ormond Street Hospital – sequence: 5 givenname: Stefanos surname: Zafeiriou fullname: Zafeiriou, Stefanos organization: Imperial College London |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31983806$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9UV1rFDEUDVKx29Uf4Isu-KIPU2-SzdeLUOpXYYtg9TlksplpyuxkTWZE_713mFrdghJCOMk59-aec0KO-tQHQp5SOKUA6nWhlEleAVUVYlOpB2RBheIVXYM4IgswDCohDT0mJ6XcAADTjD8ix5wazTXIBXm-cbkNqyvvurDib1eXKe-vXY3gMm1DVx6Th43rSnhyey7J1_fvvpx_rDafPlycn20qL9Z8qCTUsKVQe2GYCdJzxRptJDBpgDHptPaKa--CorRmBr-kHTO1N4gNF5ovyZu57n6sd2HrQz9k19l9jjuXf9rkoj186eO1bdN3K43gBveSvLwtkNO3MZTB7mLxoetcH9JYLONryYGBmKgv7lFv0ph7HM8yAKnWCtCmJTmdWS1aY2PfJOzrcW3DLnoMool4fybY5CROjYJXBwLkDOHH0LqxFHtx9fmQ--zvce_m_B0MEuhM8DmVkkNzR6Fgp_DtHL7F8CdsrEKNuqfxcXBDTJNjsfuvks3Kgl36NuQ_jvxb9AthIb0G |
| CitedBy_id | crossref_primary_10_1109_TPAMI_2021_3138762 crossref_primary_10_1145_3450626_3459806 crossref_primary_10_1016_j_prosdent_2023_02_019 crossref_primary_10_1016_j_bjps_2020_10_017 crossref_primary_10_1016_j_patcog_2022_108971 crossref_primary_10_1109_TIP_2025_3563762 crossref_primary_10_1007_s00371_025_03914_9 crossref_primary_10_1097_SCS_0000000000007875 crossref_primary_10_1109_TVCG_2023_3284500 crossref_primary_10_3390_app11188588 crossref_primary_10_1007_s00170_024_13876_2 crossref_primary_10_1097_SCS_0000000000007632 crossref_primary_10_1109_TPAMI_2021_3090942 crossref_primary_10_1093_humrep_dead006 crossref_primary_10_1002_aisy_202400159 crossref_primary_10_1016_j_inffus_2024_102869 crossref_primary_10_1145_3476576_3476645 crossref_primary_10_1016_j_apergo_2022_103933 crossref_primary_10_1007_s11263_020_01304_3 crossref_primary_10_1145_3197517_3201283 crossref_primary_10_32604_cmc_2023_035344 crossref_primary_10_1109_ACCESS_2021_3064179 crossref_primary_10_4018_IJCPS_314572 crossref_primary_10_1145_3395208 crossref_primary_10_1109_ACCESS_2023_3255099 crossref_primary_10_1016_j_dsp_2022_103628 crossref_primary_10_1016_j_ins_2021_09_024 crossref_primary_10_1109_TMM_2019_2933338 crossref_primary_10_1002_cav_2028 crossref_primary_10_1016_j_dsp_2025_104986 crossref_primary_10_1016_j_cag_2021_06_004 crossref_primary_10_1007_s11263_022_01730_5 crossref_primary_10_1016_j_cad_2023_103483 crossref_primary_10_1016_j_imavis_2021_104119 crossref_primary_10_1097_PRS_0000000000011831 crossref_primary_10_1109_TVCG_2023_3323578 crossref_primary_10_1038_s41598_021_02411_y crossref_primary_10_3390_ani13030385 crossref_primary_10_1016_j_patrec_2019_09_026 crossref_primary_10_1016_j_neucom_2024_128168 crossref_primary_10_1109_TCSVT_2023_3288903 crossref_primary_10_1145_3272127_3275075 crossref_primary_10_1109_ACCESS_2020_3031886 crossref_primary_10_1111_cgf_70170 crossref_primary_10_1016_j_bonr_2022_101528 crossref_primary_10_1186_s13638_020_01760_y crossref_primary_10_1002_cav_2044 crossref_primary_10_1007_s00381_021_05330_5 crossref_primary_10_1109_TAFFC_2022_3182342 crossref_primary_10_1527_tjsai_40_4_AG25_C crossref_primary_10_1007_s11263_019_01260_7 crossref_primary_10_1145_3397765 crossref_primary_10_1145_3592093 crossref_primary_10_1016_j_jbi_2022_104121 crossref_primary_10_1007_s00371_021_02129_y crossref_primary_10_3390_ani13071179 crossref_primary_10_1007_s00371_024_03473_5 crossref_primary_10_1109_TPAMI_2023_3253243 crossref_primary_10_1109_ACCESS_2020_2979518 crossref_primary_10_1007_s10489_023_05094_2 crossref_primary_10_1016_j_cag_2023_12_010 crossref_primary_10_1016_j_asoc_2024_112260 crossref_primary_10_1007_s10044_021_01012_9 crossref_primary_10_1016_j_engappai_2022_104669 crossref_primary_10_1109_TMM_2024_3521835 crossref_primary_10_1007_s11263_018_1134_y crossref_primary_10_1016_j_eswa_2023_119678 crossref_primary_10_1145_3550454_3555494 crossref_primary_10_1016_j_artmed_2022_102425 crossref_primary_10_1016_j_jcms_2021_02_020 crossref_primary_10_1109_JSTSP_2018_2877108 crossref_primary_10_1007_s11831_021_09705_4 crossref_primary_10_1109_TNNLS_2022_3190068 crossref_primary_10_1016_j_cviu_2021_103244 crossref_primary_10_1109_ACCESS_2024_3396632 crossref_primary_10_1109_TIP_2021_3065798 crossref_primary_10_1007_s10044_022_01060_9 crossref_primary_10_1016_j_neucom_2021_06_023 crossref_primary_10_1109_TPAMI_2024_3480151 crossref_primary_10_1016_j_cad_2022_103271 crossref_primary_10_1016_j_cviu_2022_103384 crossref_primary_10_1109_ACCESS_2022_3193386 crossref_primary_10_1097_PRS_0000000000010331 crossref_primary_10_1109_JBHI_2022_3164848 crossref_primary_10_1016_j_displa_2021_102063 crossref_primary_10_1016_j_prosdent_2024_03_006 crossref_primary_10_1016_j_displa_2024_102725 crossref_primary_10_1016_j_jcms_2023_11_006 crossref_primary_10_1007_s00371_025_04068_4 crossref_primary_10_1038_s41598_019_42533_y crossref_primary_10_1016_j_cad_2025_103888 crossref_primary_10_1111_cgf_14071 crossref_primary_10_4103_jpn_JPN_48_22 crossref_primary_10_1007_s11548_023_02858_6 crossref_primary_10_1109_TCSVT_2024_3386671 crossref_primary_10_1007_s00371_024_03319_0 crossref_primary_10_1016_j_prosdent_2025_03_002 crossref_primary_10_1016_j_sigpro_2020_107755 crossref_primary_10_1016_j_cag_2024_104096 crossref_primary_10_1007_s00784_023_05076_1 crossref_primary_10_1111_cgf_14639 crossref_primary_10_1038_s41598_019_49506_1 crossref_primary_10_1007_s11042_020_09147_3 crossref_primary_10_1007_s11042_020_09189_7 crossref_primary_10_1109_ACCESS_2024_3381975 crossref_primary_10_1109_TPAMI_2018_2832138 crossref_primary_10_1007_s11263_023_01870_2 crossref_primary_10_1007_s11263_021_01494_4 crossref_primary_10_1109_TVCG_2021_3126659 crossref_primary_10_1016_j_patrec_2022_12_027 crossref_primary_10_1109_TCSVT_2021_3136589 crossref_primary_10_1111_cgf_14760 crossref_primary_10_1109_TMM_2019_2903724 crossref_primary_10_1111_cgf_14762 crossref_primary_10_3390_jimaging3040055 crossref_primary_10_1145_3306346_3323028 crossref_primary_10_1145_3337067 crossref_primary_10_1007_s11263_023_01825_7 crossref_primary_10_3390_s20226554 crossref_primary_10_1109_LSP_2023_3238908 crossref_primary_10_1016_j_ergon_2022_103321 crossref_primary_10_1089_fpsam_2023_0030 |
| Cites_doi | 10.1109/ICCV.2013.404 10.1109/TPAMI.2012.206 10.1109/CVPR.2009.5206522 10.1145/311535.311556 10.1109/34.927467 10.1109/AFGR.2008.4813376 10.1016/j.cviu.2014.05.005 10.1109/TPAMI.2011.275 10.1007/978-3-642-03798-6_24 10.1145/1186822.1073209 10.1109/CVPR.2007.383165 10.1109/ICIP.2014.7025947 10.1007/s00138-013-0579-9 10.1109/AVSS.2009.58 10.1007/978-3-319-10590-1_20 10.1109/ICCVW.2013.59 10.1002/humu.22054 10.1109/CVPR.2011.5995602 10.1007/978-3-642-03061-1_2 10.1109/TPAMI.2003.1227983 10.5220/0005669500790086 10.1109/ICIP.2014.7025044 10.1016/j.cviu.2014.06.013 10.1109/ICCV.2015.411 10.1109/ICCV.2011.6126510 10.1007/s11263-009-0275-4 10.1109/CVPR.2015.7299095 10.1109/34.24792 10.1145/2647868.2654890 10.1109/CRV.2011.53 10.1007/s11263-009-0300-7 10.1016/j.jcms.2015.02.005 10.1016/j.neucom.2014.04.089 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2017 The Author(s) 2017. COPYRIGHT 2018 Springer International Journal of Computer Vision is a copyright of Springer, (2017). All Rights Reserved. |
| Copyright_xml | – notice: The Author(s) 2017 – notice: The Author(s) 2017. – notice: COPYRIGHT 2018 Springer – notice: International Journal of Computer Vision is a copyright of Springer, (2017). All Rights Reserved. |
| DBID | C6C AAYXX CITATION NPM ISR 3V. 7SC 7WY 7WZ 7XB 87Z 8AL 8FD 8FE 8FG 8FK 8FL ABUWG AFKRA ARAPS AZQEC BENPR BEZIV BGLVJ CCPQU DWQXO FRNLG F~G GNUQQ HCIFZ JQ2 K60 K6~ K7- L.- L7M L~C L~D M0C M0N P5Z P62 PHGZM PHGZT PKEHL PQBIZ PQBZA PQEST PQGLB PQQKQ PQUKI PYYUZ Q9U 7X8 5PM |
| DOI | 10.1007/s11263-017-1009-7 |
| DatabaseName | Springer Nature OA/Free Journals CrossRef PubMed Gale In Context: Science ProQuest Central (Corporate) Computer and Information Systems Abstracts ABI/INFORM Collection ABI/INFORM Global (PDF only) ProQuest Central (purchase pre-March 2016) ABI/INFORM Global (Alumni Edition) Computing Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ABI/INFORM Collection (Alumni Edition) ProQuest Central (Alumni Edition) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Business Premium Collection Technology Collection ProQuest One Community College ProQuest Central Korea Business Premium Collection (Alumni) ABI/INFORM Global (Corporate) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection ProQuest Business Collection (Alumni Edition) ProQuest Business Collection Computer Science Database ABI/INFORM Professional Advanced Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ABI/INFORM Global Computing Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Business ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ABI/INFORM Collection China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef PubMed ABI/INFORM Global (Corporate) ProQuest Business Collection (Alumni Edition) ProQuest One Business Computer Science Database ProQuest Central Student Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ABI/INFORM Complete ProQuest Central ABI/INFORM Professional Advanced ProQuest One Applied & Life Sciences ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace ABI/INFORM Complete (Alumni Edition) Advanced Technologies & Aerospace Collection Business Premium Collection ABI/INFORM Global ProQuest Computing ABI/INFORM Global (Alumni Edition) ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ABI/INFORM China ProQuest Technology Collection ProQuest SciTech Collection ProQuest Business Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest One Business (Alumni) ProQuest One Academic ProQuest Central (Alumni) ProQuest One Academic (New) Business Premium Collection (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic PubMed ABI/INFORM Global (Corporate) |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: BENPR name: ProQuest Central (subscription) url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Applied Sciences Computer Science |
| EISSN | 1573-1405 |
| EndPage | 254 |
| ExternalDocumentID | PMC6953995 A528380929 31983806 10_1007_s11263_017_1009_7 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: Engineering and Physical Sciences Research Council grantid: EP/J017787/1; EP/N007743/1 funderid: http://dx.doi.org/10.13039/501100000266 – fundername: Engineering and Physical Sciences Research Council grantid: DTA funderid: http://dx.doi.org/10.13039/501100000266 – fundername: ; grantid: DTA – fundername: ; grantid: EP/J017787/1; EP/N007743/1 |
| GroupedDBID | -4Z -59 -5G -BR -EM -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 199 1N0 1SB 2.D 203 28- 29J 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 3V. 4.4 406 408 409 40D 40E 5GY 5QI 5VS 67Z 6NX 6TJ 78A 7WY 8FE 8FG 8FL 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDBF ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFO ACGFS ACHSB ACHXU ACIHN ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACREN ACUHS ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADYOE ADZKW AEAQA AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFYQB AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMTXH AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN AZQEC B-. B0M BA0 BBWZM BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ BSONS C6C CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 DWQXO EAD EAP EAS EBLON EBS EDO EIOEI EJD EMK EPL ESBYG ESX F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GROUPED_ABI_INFORM_COMPLETE GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IAO IHE IJ- IKXTQ ISR ITC ITM IWAJR IXC IZIGR IZQ I~X I~Y I~Z J-C J0Z JBSCW JCJTX JZLTJ K60 K6V K6~ K7- KDC KOV KOW LAK LLZTM M0C M0N M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P2P P62 P9O PF0 PQBIZ PQBZA PQQKQ PROAC PT4 PT5 QF4 QM1 QN7 QO4 QOK QOS R4E R89 R9I RHV RNI RNS ROL RPX RSV RZC RZE RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TAE TEORI TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z7R Z7S Z7V Z7W Z7X Z7Y Z7Z Z83 Z86 Z88 Z8M Z8N Z8P Z8Q Z8R Z8S Z8T Z8W Z92 ZMTXR ~8M ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG ADKFA AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION ICD PHGZM PHGZT PQGLB NPM 7SC 7XB 8AL 8FD 8FK JQ2 L.- L7M L~C L~D PKEHL PQEST PQUKI Q9U 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c543t-60b0d10bc5929e6c372f89602690226a88c738cae711b296918a29bc971193583 |
| IEDL.DBID | K7- |
| ISICitedReferencesCount | 230 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000425619100006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0920-5691 |
| IngestDate | Tue Nov 04 02:02:49 EST 2025 Thu Oct 02 09:54:05 EDT 2025 Tue Nov 04 22:56:54 EST 2025 Sat Nov 29 09:51:27 EST 2025 Wed Nov 26 10:06:29 EST 2025 Thu Apr 03 06:59:51 EDT 2025 Tue Nov 18 20:15:33 EST 2025 Sat Nov 29 06:42:27 EST 2025 Fri Feb 21 02:35:17 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2-4 |
| Keywords | 3D morphable models Demographic-specific models Dense correspondence |
| Language | English |
| License | The Author(s) 2017. Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c543t-60b0d10bc5929e6c372f89602690226a88c738cae711b296918a29bc971193583 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Communicated by Edmond Boyer, Cordelia Schmid. |
| ORCID | 0000-0003-2114-9595 |
| OpenAccessLink | https://link.springer.com/10.1007/s11263-017-1009-7 |
| PMID | 31983806 |
| PQID | 2006747082 |
| PQPubID | 1456341 |
| PageCount | 22 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_6953995 proquest_miscellaneous_2346302055 proquest_journals_2006747082 gale_infotracacademiconefile_A528380929 gale_incontextgauss_ISR_A528380929 pubmed_primary_31983806 crossref_primary_10_1007_s11263_017_1009_7 crossref_citationtrail_10_1007_s11263_017_1009_7 springer_journals_10_1007_s11263_017_1009_7 |
| PublicationCentury | 2000 |
| PublicationDate | 2018-04-01 |
| PublicationDateYYYYMMDD | 2018-04-01 |
| PublicationDate_xml | – month: 04 year: 2018 text: 2018-04-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York – name: United States |
| PublicationTitle | International journal of computer vision |
| PublicationTitleAbbrev | Int J Comput Vis |
| PublicationTitleAlternate | Int J Comput Vis |
| PublicationYear | 2018 |
| Publisher | Springer US Springer Springer Nature B.V |
| Publisher_xml | – name: Springer US – name: Springer – name: Springer Nature B.V |
| References | Bolkart, T., Brunton, A., Salazar, A., & Wuhrer, S. (2013). Website of statistical 3d shape models of human faces. http://statistical-face-models.mmci.uni-saarland.de/. Huber, P., Hu, G., Tena, R., Mortazavian, P., Koppen, W. P., Christmas, W., Rätsch, M., & Kittler, J. (2016). A multiresolution 3d morphable face model and fitting framework. In: Proceedings of the 11th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Paysan, P., Knothe, R., Amberg, B., Romdhani, S., & Vetter, T. (2009). Website of basel face model. http://faces.cs.unibas.ch/bfm/. Bolkart, T., & Wuhrer, S. (2015). A groupwise multilinear correspondence optimization for 3d faces. In: IEEE International Conference on Computer Vision (ICCV). Amberg, B., Knothe, R., & Vetter, T. (2008). Expression invariant 3D face recognition with a morphable model. In 8th IEEE international conference on automatic face & gesture recognition FG’08, pp. 1–6. IEEE. Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pp. 248–255. IEEE. SalazarAWuhrerSShuCPrietoFFully automatic expression-invariant face correspondenceMachine Vision and Applications201425485987910.1007/s00138-013-0579-9 Van der MaatenLHintonGVisualizing data using t-SNEJournal of Machine Learning Research200892579–2605851225.68219 BolkartTWuhrerS3D faces in motion: Fully automatic registration and statistical analysisComputer Vision and Image Understanding201513110011510.1016/j.cviu.2014.06.013 Brunton, A., & Bolkart, T., & Wuhrer, S. (2014). Multilinear wavelets: A statistical shape space for human faces. In: European Conference on Computer Vision (ECCV), pp. 297–312. Springer. Cosker, D., Krumhuber, E., & Hilton, A. (2011). A facs valid 3d dynamic action unit database with applications to 3d dynamic morphable facial modeling. In: Computer Vision (ICCV), 2011 IEEE International Conference on, pp. 2296–2303. IEEE. Antonakos, E., Alabort-i Medina, J., Tzimiropoulos, G., & Zafeiriou, S. (2014). Hog active appearance models. In IEEE international conference on image processing (ICIP), pp. 224–228. IEEE. DuanFHuangDTianYLuKWuZZhouM3d face reconstruction from skull by regression modeling in shape parameter spacesNeurocomputing201515167468210.1016/j.neucom.2014.04.089 Amberg, B., Romdhani, S., & Vetter, T. (2007). Optimal step nonrigid icp algorithms for surface registration. In IEEE conference on computer vision and pattern recognition CVPR’07, pp. 1–8. IEEE. DaviesRTaylorCStatistical models of shape: Optimisation and evaluation2008BerlinSpringer1161.68761 KingDEDlib-ml: A machine learning toolkitJournal of Machine Learning Research20091017551758 BlanzVVetterTFace recognition based on fitting a 3d morphable modelPattern Analysis and Machine Intelligence, IEEE Transactions on20032591063107410.1109/TPAMI.2003.1227983 Blanz, V., & Vetter, T. (1999). A morphable model for the synthesis of 3d faces. In: Proceedings of the 26th annual conference on Computer graphics and interactive techniques, pp. 187–194. ACM Press/Addison-Wesley Publishing Co. Alabort-i Medina, J., Antonakos, E., Booth, J., Snape, P., & Zafeiriou, S. (2014). Menpo: A comprehensive platform for parametric image alignment and visual deformable models. In Proceedings of the ACM international conference on multimedia, MM ’14, pp. 679–682. ACM, New York, NY, USA. doi:10.1145/2647868.2654890. Zulqarnain Gilani, S., Shafait, F., & Mian, A. (2015). Shape-based automatic detection of a large number of 3D facial landmarks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4639–4648. BooksteinFLPrincipal warps: Thin-plate splines and the decomposition of deformationsIEEE Transactions on pattern analysis and machine intelligence198911656758510.1109/34.247920691.65002 Jain, V., & Learned-Miller, E. G. (2010). Fddb: A benchmark for face detection in unconstrained settings. UMass Amherst Technical Report. Kemelmacher-Shlizerman, I. (2013). Internet based morphable model. In: 2013 IEEE international conference on computer vision (ICCV), pp. 3256–3263. IEEE. StaalFCPonniahAJAngulliaFRuffCKoudstaalMJDunawayDDescribing Crouzon and Pfeiffer syndrome based on principal component analysisJournal of Cranio-Maxillofacial Surgery201543452853610.1016/j.jcms.2015.02.005 Vlasic, D., Brand, M., Pfister, H., & Popović, J. (2005). Face transfer with multilinear models. In ACM transactions on graphics (TOG) (Vol. 24, pp. 426–433). ACM. Wedel, A., Pock, T., Zach, C., Bischof, H., & Cremers, D. (2009). An improved algorithm for TV-L1 optical flow. In Statistical and geometrical approaches to visual motion analysis. Lecture Notes in Computer Science (pp. 23–45). Berlin: Springer. TodericiGOmalleySMPassalisGTheoharisTKakadiarisIAEthnicity-and gender-based subject retrieval using 3-D face-recognition techniquesInternational Journal of Computer Vision2010892–338239110.1007/s11263-009-0300-7 Belhumeur, P. N., Jacobs, D.W., Kriegman, D., & Kumar, N. (2011). Localizing parts of faces using a consensus of exemplars. In: Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pp. 545–552. IEEE. Brunton, A., Lang, J., Dubois, E., & Shu, C. (2011). Wavelet model-based stereo for fast, robust face reconstruction. In: Canadian Conference on Computer and Robot Vision (CRV), pp. 347–354. Patel, A., & Smith, W. A. (2009). 3d morphable face models revisited. In 2009 IEEE conference on computer vision and pattern recognition, CVPR, pp. 1327–1334. IEEE. EveringhamMVan GoolLWilliamsCKWinnJZissermanAThe pascal visual object classes (voc) challengeInternational journal of computer vision201088230333810.1007/s11263-009-0275-4 HeoJSavvidesMGender and ethnicity specific generic elastic models from a single 2d image for novel 2d pose face synthesis and recognitionPattern Analysis and Machine Intelligence, IEEE Transactions on201234122341235010.1109/TPAMI.2011.275 Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., & Pantic, M. (2013). 300 faces in-the-wild challenge: The first facial landmark localization challenge. In 2013 IEEE international conference on computer vision workshops (ICCVW), pp. 397–403. IEEE. AldrianOSmithWAInverse rendering of faces with a 3D morphable modelIEEE Transactions on Pattern Analysis and Machine Intelligence20133551080109310.1109/TPAMI.2012.206 HammondPSuttieMLarge-scale objective phenotyping of 3d facial morphologyHuman mutation201233581782510.1002/humu.22054 Paysan, P., Lüthi, M., Albrecht, T., Lerch, A., Amberg, B., Santini, F., & Vetter, T. (2009). Face reconstruction from skull shapes and physical attributes. In DAGM-symposium, pp. 232–241. Springer. Booth, J., & Zafeiriou, S. (2014). Optimal uv spaces for facial morphable model construction. In: Image Processing (ICIP), 2014 IEEE International Conference on, pp. 4672–4676. IEEE. BruntonASalazarABolkartTWuhrerSReview of statistical shape spaces for 3d data with comparative analysis for human facesComputer Vision and Image Understanding201412811710.1016/j.cviu.2014.05.005 Paysan, P., Knothe, R., Amberg, B., Romdhani, S., & Vetter, T. (2009). A 3D face model for pose and illumination invariant face recognition. In Sixth IEEE international conference on advanced video and signal based surveillance, AVSS’09, pp. 296–301. IEEE. CootesTFEdwardsGJTaylorCJActive appearance modelsIEEE Transactions on pattern analysis and machine intelligence200123668168510.1109/34.927467 T Bolkart (1009_CR10) 2015; 131 R Davies (1009_CR19) 2008 A Brunton (1009_CR16) 2014; 128 M Everingham (1009_CR22) 2010; 88 1009_CR38 FL Bookstein (1009_CR12) 1989; 11 1009_CR15 F Duan (1009_CR21) 2015; 151 1009_CR14 1009_CR13 1009_CR18 1009_CR39 1009_CR30 DE King (1009_CR28) 2009; 10 1009_CR11 1009_CR33 1009_CR32 1009_CR31 TF Cootes (1009_CR17) 2001; 23 L Maaten Van der (1009_CR37) 2008; 9 P Hammond (1009_CR23) 2012; 33 G Toderici (1009_CR36) 2010; 89 FC Staal (1009_CR35) 2015; 43 O Aldrian (1009_CR2) 2013; 35 1009_CR6 1009_CR7 1009_CR9 1009_CR27 1009_CR3 1009_CR26 1009_CR4 1009_CR25 1009_CR5 J Heo (1009_CR24) 2012; 34 1009_CR29 1009_CR1 1009_CR40 A Salazar (1009_CR34) 2014; 25 V Blanz (1009_CR8) 2003; 25 1009_CR20 |
| References_xml | – reference: BolkartTWuhrerS3D faces in motion: Fully automatic registration and statistical analysisComputer Vision and Image Understanding201513110011510.1016/j.cviu.2014.06.013 – reference: Paysan, P., Knothe, R., Amberg, B., Romdhani, S., & Vetter, T. (2009). Website of basel face model. http://faces.cs.unibas.ch/bfm/. – reference: KingDEDlib-ml: A machine learning toolkitJournal of Machine Learning Research20091017551758 – reference: Patel, A., & Smith, W. A. (2009). 3d morphable face models revisited. In 2009 IEEE conference on computer vision and pattern recognition, CVPR, pp. 1327–1334. IEEE. – reference: Vlasic, D., Brand, M., Pfister, H., & Popović, J. (2005). Face transfer with multilinear models. In ACM transactions on graphics (TOG) (Vol. 24, pp. 426–433). ACM. – reference: AldrianOSmithWAInverse rendering of faces with a 3D morphable modelIEEE Transactions on Pattern Analysis and Machine Intelligence20133551080109310.1109/TPAMI.2012.206 – reference: DuanFHuangDTianYLuKWuZZhouM3d face reconstruction from skull by regression modeling in shape parameter spacesNeurocomputing201515167468210.1016/j.neucom.2014.04.089 – reference: TodericiGOmalleySMPassalisGTheoharisTKakadiarisIAEthnicity-and gender-based subject retrieval using 3-D face-recognition techniquesInternational Journal of Computer Vision2010892–338239110.1007/s11263-009-0300-7 – reference: BooksteinFLPrincipal warps: Thin-plate splines and the decomposition of deformationsIEEE Transactions on pattern analysis and machine intelligence198911656758510.1109/34.247920691.65002 – reference: Brunton, A., Lang, J., Dubois, E., & Shu, C. (2011). Wavelet model-based stereo for fast, robust face reconstruction. In: Canadian Conference on Computer and Robot Vision (CRV), pp. 347–354. – reference: Paysan, P., Knothe, R., Amberg, B., Romdhani, S., & Vetter, T. (2009). A 3D face model for pose and illumination invariant face recognition. In Sixth IEEE international conference on advanced video and signal based surveillance, AVSS’09, pp. 296–301. IEEE. – reference: BruntonASalazarABolkartTWuhrerSReview of statistical shape spaces for 3d data with comparative analysis for human facesComputer Vision and Image Understanding201412811710.1016/j.cviu.2014.05.005 – reference: Amberg, B., Knothe, R., & Vetter, T. (2008). Expression invariant 3D face recognition with a morphable model. In 8th IEEE international conference on automatic face & gesture recognition FG’08, pp. 1–6. IEEE. – reference: Amberg, B., Romdhani, S., & Vetter, T. (2007). Optimal step nonrigid icp algorithms for surface registration. In IEEE conference on computer vision and pattern recognition CVPR’07, pp. 1–8. IEEE. – reference: Blanz, V., & Vetter, T. (1999). A morphable model for the synthesis of 3d faces. In: Proceedings of the 26th annual conference on Computer graphics and interactive techniques, pp. 187–194. ACM Press/Addison-Wesley Publishing Co. – reference: Cosker, D., Krumhuber, E., & Hilton, A. (2011). A facs valid 3d dynamic action unit database with applications to 3d dynamic morphable facial modeling. In: Computer Vision (ICCV), 2011 IEEE International Conference on, pp. 2296–2303. IEEE. – reference: HammondPSuttieMLarge-scale objective phenotyping of 3d facial morphologyHuman mutation201233581782510.1002/humu.22054 – reference: HeoJSavvidesMGender and ethnicity specific generic elastic models from a single 2d image for novel 2d pose face synthesis and recognitionPattern Analysis and Machine Intelligence, IEEE Transactions on201234122341235010.1109/TPAMI.2011.275 – reference: Bolkart, T., Brunton, A., Salazar, A., & Wuhrer, S. (2013). Website of statistical 3d shape models of human faces. http://statistical-face-models.mmci.uni-saarland.de/. – reference: Bolkart, T., & Wuhrer, S. (2015). A groupwise multilinear correspondence optimization for 3d faces. In: IEEE International Conference on Computer Vision (ICCV). – reference: Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., & Pantic, M. (2013). 300 faces in-the-wild challenge: The first facial landmark localization challenge. In 2013 IEEE international conference on computer vision workshops (ICCVW), pp. 397–403. IEEE. – reference: StaalFCPonniahAJAngulliaFRuffCKoudstaalMJDunawayDDescribing Crouzon and Pfeiffer syndrome based on principal component analysisJournal of Cranio-Maxillofacial Surgery201543452853610.1016/j.jcms.2015.02.005 – reference: SalazarAWuhrerSShuCPrietoFFully automatic expression-invariant face correspondenceMachine Vision and Applications201425485987910.1007/s00138-013-0579-9 – reference: BlanzVVetterTFace recognition based on fitting a 3d morphable modelPattern Analysis and Machine Intelligence, IEEE Transactions on20032591063107410.1109/TPAMI.2003.1227983 – reference: Huber, P., Hu, G., Tena, R., Mortazavian, P., Koppen, W. P., Christmas, W., Rätsch, M., & Kittler, J. (2016). A multiresolution 3d morphable face model and fitting framework. In: Proceedings of the 11th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. – reference: Alabort-i Medina, J., Antonakos, E., Booth, J., Snape, P., & Zafeiriou, S. (2014). Menpo: A comprehensive platform for parametric image alignment and visual deformable models. In Proceedings of the ACM international conference on multimedia, MM ’14, pp. 679–682. ACM, New York, NY, USA. doi:10.1145/2647868.2654890. – reference: Van der MaatenLHintonGVisualizing data using t-SNEJournal of Machine Learning Research200892579–2605851225.68219 – reference: Brunton, A., & Bolkart, T., & Wuhrer, S. (2014). Multilinear wavelets: A statistical shape space for human faces. In: European Conference on Computer Vision (ECCV), pp. 297–312. Springer. – reference: Zulqarnain Gilani, S., Shafait, F., & Mian, A. (2015). Shape-based automatic detection of a large number of 3D facial landmarks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4639–4648. – reference: Belhumeur, P. N., Jacobs, D.W., Kriegman, D., & Kumar, N. (2011). Localizing parts of faces using a consensus of exemplars. In: Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pp. 545–552. IEEE. – reference: Booth, J., & Zafeiriou, S. (2014). Optimal uv spaces for facial morphable model construction. In: Image Processing (ICIP), 2014 IEEE International Conference on, pp. 4672–4676. IEEE. – reference: DaviesRTaylorCStatistical models of shape: Optimisation and evaluation2008BerlinSpringer1161.68761 – reference: Kemelmacher-Shlizerman, I. (2013). Internet based morphable model. In: 2013 IEEE international conference on computer vision (ICCV), pp. 3256–3263. IEEE. – reference: Paysan, P., Lüthi, M., Albrecht, T., Lerch, A., Amberg, B., Santini, F., & Vetter, T. (2009). Face reconstruction from skull shapes and physical attributes. In DAGM-symposium, pp. 232–241. Springer. – reference: Wedel, A., Pock, T., Zach, C., Bischof, H., & Cremers, D. (2009). An improved algorithm for TV-L1 optical flow. In Statistical and geometrical approaches to visual motion analysis. Lecture Notes in Computer Science (pp. 23–45). Berlin: Springer. – reference: Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pp. 248–255. IEEE. – reference: CootesTFEdwardsGJTaylorCJActive appearance modelsIEEE Transactions on pattern analysis and machine intelligence200123668168510.1109/34.927467 – reference: Antonakos, E., Alabort-i Medina, J., Tzimiropoulos, G., & Zafeiriou, S. (2014). Hog active appearance models. In IEEE international conference on image processing (ICIP), pp. 224–228. IEEE. – reference: Jain, V., & Learned-Miller, E. G. (2010). Fddb: A benchmark for face detection in unconstrained settings. UMass Amherst Technical Report. – reference: EveringhamMVan GoolLWilliamsCKWinnJZissermanAThe pascal visual object classes (voc) challengeInternational journal of computer vision201088230333810.1007/s11263-009-0275-4 – ident: 1009_CR27 doi: 10.1109/ICCV.2013.404 – volume: 35 start-page: 1080 issue: 5 year: 2013 ident: 1009_CR2 publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/TPAMI.2012.206 – ident: 1009_CR29 doi: 10.1109/CVPR.2009.5206522 – ident: 1009_CR7 doi: 10.1145/311535.311556 – ident: 1009_CR9 – ident: 1009_CR26 – volume: 23 start-page: 681 issue: 6 year: 2001 ident: 1009_CR17 publication-title: IEEE Transactions on pattern analysis and machine intelligence doi: 10.1109/34.927467 – ident: 1009_CR3 doi: 10.1109/AFGR.2008.4813376 – volume: 128 start-page: 1 year: 2014 ident: 1009_CR16 publication-title: Computer Vision and Image Understanding doi: 10.1016/j.cviu.2014.05.005 – volume: 34 start-page: 2341 issue: 12 year: 2012 ident: 1009_CR24 publication-title: Pattern Analysis and Machine Intelligence, IEEE Transactions on doi: 10.1109/TPAMI.2011.275 – ident: 1009_CR32 doi: 10.1007/978-3-642-03798-6_24 – ident: 1009_CR38 doi: 10.1145/1186822.1073209 – ident: 1009_CR4 doi: 10.1109/CVPR.2007.383165 – ident: 1009_CR13 doi: 10.1109/ICIP.2014.7025947 – volume: 25 start-page: 859 issue: 4 year: 2014 ident: 1009_CR34 publication-title: Machine Vision and Applications doi: 10.1007/s00138-013-0579-9 – ident: 1009_CR30 doi: 10.1109/AVSS.2009.58 – ident: 1009_CR14 doi: 10.1007/978-3-319-10590-1_20 – ident: 1009_CR33 doi: 10.1109/ICCVW.2013.59 – volume: 33 start-page: 817 issue: 5 year: 2012 ident: 1009_CR23 publication-title: Human mutation doi: 10.1002/humu.22054 – ident: 1009_CR6 doi: 10.1109/CVPR.2011.5995602 – ident: 1009_CR39 doi: 10.1007/978-3-642-03061-1_2 – volume: 25 start-page: 1063 issue: 9 year: 2003 ident: 1009_CR8 publication-title: Pattern Analysis and Machine Intelligence, IEEE Transactions on doi: 10.1109/TPAMI.2003.1227983 – volume-title: Statistical models of shape: Optimisation and evaluation year: 2008 ident: 1009_CR19 – ident: 1009_CR25 doi: 10.5220/0005669500790086 – ident: 1009_CR5 doi: 10.1109/ICIP.2014.7025044 – volume: 131 start-page: 100 year: 2015 ident: 1009_CR10 publication-title: Computer Vision and Image Understanding doi: 10.1016/j.cviu.2014.06.013 – ident: 1009_CR11 doi: 10.1109/ICCV.2015.411 – ident: 1009_CR18 doi: 10.1109/ICCV.2011.6126510 – ident: 1009_CR20 – volume: 88 start-page: 303 issue: 2 year: 2010 ident: 1009_CR22 publication-title: International journal of computer vision doi: 10.1007/s11263-009-0275-4 – ident: 1009_CR40 doi: 10.1109/CVPR.2015.7299095 – volume: 9 start-page: 85 issue: 2579–2605 year: 2008 ident: 1009_CR37 publication-title: Journal of Machine Learning Research – volume: 11 start-page: 567 issue: 6 year: 1989 ident: 1009_CR12 publication-title: IEEE Transactions on pattern analysis and machine intelligence doi: 10.1109/34.24792 – ident: 1009_CR1 doi: 10.1145/2647868.2654890 – ident: 1009_CR15 doi: 10.1109/CRV.2011.53 – volume: 89 start-page: 382 issue: 2–3 year: 2010 ident: 1009_CR36 publication-title: International Journal of Computer Vision doi: 10.1007/s11263-009-0300-7 – volume: 10 start-page: 1755 year: 2009 ident: 1009_CR28 publication-title: Journal of Machine Learning Research – ident: 1009_CR31 – volume: 43 start-page: 528 issue: 4 year: 2015 ident: 1009_CR35 publication-title: Journal of Cranio-Maxillofacial Surgery doi: 10.1016/j.jcms.2015.02.005 – volume: 151 start-page: 674 year: 2015 ident: 1009_CR21 publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.04.089 |
| SSID | ssj0002823 |
| Score | 2.6569204 |
| Snippet | We present large scale facial model (LSFM)—a 3D Morphable Model (3DMM) automatically constructed from 9663 distinct facial identities. To the best of our... We present large scale facial model (LSFM)-a 3D Morphable Model (3DMM) automatically constructed from 9663 distinct facial identities. To the best of our... |
| SourceID | pubmedcentral proquest gale pubmed crossref springer |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 233 |
| SubjectTerms | 3-D technology Age Artificial Intelligence Automation Computer Imaging Computer Science Demographics Ethnicity Image Processing and Computer Vision Pattern Recognition Pattern Recognition and Graphics Pipeline construction Population (statistical) Scale (ratio) State of the art Three dimensional models Vision Well construction |
| SummonAdditionalLinks | – databaseName: SpringerLink Contemporary (1997 - Present) dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3ra9swED-6dh_2ZX3sUfc1twwGGwbbsvX4WPqgha6UZBv9ZiRFXgvBGXGyv393tuwsYRusXwyyz4p0Pt2dcqffAbwXzuiMxzoq3YhFWZmbyKRmFKnSShdbMrENZP6NuL2V9_fqzp_jrrts9y4k2WjqxWG3JKWYI2rVhP7RF89gA62dpNU4GH7r1S_uIdr68bgvyrlKulDmn7pYMkarKvk3m7SaL7kSNG1s0eXmk2axBS-96xmetrKyDWuu2oFN74aGfpHXeKur9NDdewXvbihhHNs49pCdh58n-Hno0FVItdTG9Wv4ennx5ewq8qUVIptnbBbx2MSjJDY2R_fIcctEWkpF5agUGnWupbSCSaudSBKTKmSj1KkyVmGbIqfsDaxXk8rtQjhCp1CVmUul1hnDa8ZRcbE8M1bE3OUBxB2PC-txx6n8xbhYICYTTwrkCbVVIQL42L_yowXd-BfxCX24gsAsKsqW-a7ndV1cDwfFKSHXSJQEFcAHT1RO8Met9ocPcAqEf7VEedAJQOGXc021Ojnuu9BdCuC4f4wLkaIrunKTOdKwjDN0vnOc8dtWXvrBo56j7nkAYkmSegIC-V5-Uj0-NGDfXBF2MPb5qZOnxbD-ypO9_6LehxfoBso2H-kA1mfTuTuE5_bn7LGeHjWr6xf2bBln priority: 102 providerName: Springer Nature |
| Title | Large Scale 3D Morphable Models |
| URI | https://link.springer.com/article/10.1007/s11263-017-1009-7 https://www.ncbi.nlm.nih.gov/pubmed/31983806 https://www.proquest.com/docview/2006747082 https://www.proquest.com/docview/2346302055 https://pubmed.ncbi.nlm.nih.gov/PMC6953995 |
| Volume | 126 |
| WOSCitedRecordID | wos000425619100006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1573-1405 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002823 issn: 0920-5691 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpR1db9NAzGIbD7xsfBMYJSAkJFBEkkvu4wmNsQnEVlUdHx0v0eVyGZOmZCwtvx87uaS0EnvhxdIlbhqffbYv9tkAL4XNdcJDHZS2YEFSpnmQx3kRqNJIGxoysW3J_CMxHsvZTE3cB7fGpVX2OrFV1EVt6Bv5W9r6ouuLFuvd5a-AukZRdNW10NiArSiOI5LzzyIYNDFuJ7pW8rhFSrmK-qhme3QuiimCiTo6oviAWLFL69r5L_O0njq5Fj9tzdLhzv8SdBu2nUPq73USdAdu2Oou7Djn1HdLv8FLff-H_to9eHZEaeQ4RjJ89sE_rpFpdBTLpw5rF819-Hp48GX_Y-AaLgQmTdg84GEeFlGYmxSdJssNE3EpFTWpUmjquZbSCCaNtiKK8ljhjEodq9woHFM8lT2Azaqu7CPwC3QVVZnYWGqdMIQJR3XG0iQ3IuQ29SDspzszrho5NcW4yJZ1lIlDGXKIxioTHrwefnLZleK4DvkF8TCjEhcV5dCc6UXTZJ9Optke1bORKBTKg1cOqazxz412RxKQBKqKtYK52zMxc4u8yZYc9OD5cBuXJ8VcdGXrBeKwhDN0yVOk-GEnOsPLo_ajx3MPxIpQDQhU-nv1TnX-sy0BzhVVFMZnvunFb_la_5yTx9cT8QRuoTcou7SkXdicXy3sU7hpfs_Pm6sRbIjvpwRncgRb7w_Gk-moXW0Ij8N9hJP0B8Lpybc_KdsqOQ |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VgkQvlGcJFBoQCKkoIokdxz4gVLVUXXW7QlCk3ozjOFCpSkqzC-JP8RuZyWvZleitBy4rOZk4tvN5HjvjGYAXqcsMF6EJCpezgBdJFmRxlgeqsNKFlkRskzJ_nE4m8uREfViB3_1ZGAqr7Hliw6jzytJ_5G_I9EXVFyXWu_PvAVWNIu9qX0KjhcWh-_UTTbb67WgPv-_LON5_f7x7EHRVBQKbcDYNRJiFeRRmNkHNwAnL0riQiioxKZRnwkhpUyatcWkUZbESKpImVplV2CanIcN-r8F1znE7UKhguDtwfjRf2tL1aJIl-GDvRW2O6kUxeUxRJkTkj0gX5OCyNPhLHC6Hai75axsxuL_-vy3gbbjVKdz-TrtD7sCKK-_Ceqd8-x1rq_FSX9-iv3YPtsYUJo9tXDaf7flHFYKSjpr5VEHurL4Pn69k5A9gtaxK9xD8HFVhVXAXS2M4w18ukF2zhGc2DYVLPAj7z6ttl22din6c6XmeaEKERkRQW-nUg-3hkfM21chlxM8JM5pSeJQUI_TVzOpajz591DuUr0ciCJUHrzqiosKXW9MducApUNavBcrNHjS6Y2K1niPGg2fDbWQ_5FMypatmSMO4YGhyJDjjjRaqw-CRu1P3woN0AcQDAaU2X7xTnn5rUpwLRRmTsc_XPdznw_rnmjy6fBJbcPPg-Gisx6PJ4WNYQ81XtiFYm7A6vZi5J3DD_pie1hdPm13tw5er3gV_ACURfCM |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Zb9QwEB6VglBfKDeBQgMCIRVFTeLExwNCFcuK1S6rikPqm3Ecp1SqktLsgvhr_DpmcuyyK9G3PvASycnE8fF5jow9A_BcuMwkPDRB4XIWJEWaBVmc5YEqrHShJRHbhMyfiOlUHh2pww343Z-FoW2VPU9sGHVeWfpHvk-mL6q-KLH2i25bxOFg-Obse0AZpMjT2qfTaCEydr9-ovlWvx4NcK5fxPHw3ee374Muw0Bg04TNAh5mYR6FmU1RS3DcMhEXUlFWJoWyjRsprWDSGieiKIsVV5E0scqswjI5EBnWewWuohROaY2NRbCQAmjKtGns0TxL8cXeo9oc24ti8p6ifIjINyFWZOK6ZPhLNK5v21zz3TYicbj9Pw_mTbjRKeL-QbtybsGGK2_DdqeU-x3Lq_FWn_eiv3cHdie0fR7LOIQ-G_gfKgQrHUHzKbPcaX0XvlxKy-_BZlmV7gH4OarIqkhcLI1JGF4TjmycpUlmRchd6kHYT7W2XRR2SgZyqpfxowkdGtFBZaWFB3uLV87aECQXET8j_GgK7VHSvB6beV3r0aeP-oDi-EgEpPLgZUdUVPhxa7qjGNgFiga2QrnTA0h3zK3WS_R48HTxGNkS-ZpM6ao50rCEMzRFUuzx_Ra2i8Yj16fquQdiBdALAgp5vvqkPPnWhD7niiIpY52veugvm_XPMXl4cSd24TqCX09G0_Ej2EKFWLY7s3Zgc3Y-d4_hmv0xO6nPnzQL3Ievl70I_gB4SoTJ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Large+Scale+3D+Morphable+Models&rft.jtitle=International+journal+of+computer+vision&rft.au=Booth%2C+James&rft.au=Roussos%2C+Anastasios&rft.au=Ponniah%2C+Allan&rft.au=Dunaway%2C+David&rft.date=2018-04-01&rft.issn=0920-5691&rft.volume=126&rft.issue=2&rft.spage=233&rft_id=info:doi/10.1007%2Fs11263-017-1009-7&rft_id=info%3Apmid%2F31983806&rft.externalDocID=31983806 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0920-5691&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0920-5691&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0920-5691&client=summon |