Action unit intensity regression for facial MoCap aimed towards digital humans.
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| Title: | Action unit intensity regression for facial MoCap aimed towards digital humans. |
|---|---|
| Authors: | Vilchis, Carlos1,2 (AUTHOR) carlos.vilchis@tec.mx, Mendez-Ruiz, Mauricio2 (AUTHOR) mauricio@eugenia.tech, Perez-Guerrero, Carmina2 (AUTHOR) carmina@eugenia.tech, Gonzalez-Mendoza, Miguel1 (AUTHOR) mgonza@tec.mx |
| Source: | Multimedia Tools & Applications. Apr2025, Vol. 84 Issue 13, p11775-11794. 20p. |
| Subjects: | Motion capture (Cinematography), Artificial intelligence, Image processing, Support vector machines, Machine learning, Avatars (Virtual reality) |
| Abstract: | Due to the increasing demand for virtual avatars, there has been a recent growth in the research and development of frameworks for realistic digital humans, which create a demand for realistic and adaptable facial motion capture systems. Most frameworks belong to private companies or represent high investments, which is why the creation of democratized solutions is relevant for the growth of digital human content creation. This research work proposes a facial motion capture framework for digital humans with the use of machine learning for facial codification intensity regression. The main focus is to use coded face movement intensities to generate realistic expressions on a digital human. The ablation studies performed on the regression models show that Neural Networks, using Histogram of Oriented Gradients as features, and with person-specific normalization, present overall better performance against other methods in the literature. With an RMSE of 0.052, the proposed framework offers reliable results that can be rendered in the face of a MetaHuman. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 184914417 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Action unit intensity regression for facial MoCap aimed towards digital humans. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Vilchis%2C+Carlos%22">Vilchis, Carlos</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> carlos.vilchis@tec.mx</i><br /><searchLink fieldCode="AR" term="%22Mendez-Ruiz%2C+Mauricio%22">Mendez-Ruiz, Mauricio</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> mauricio@eugenia.tech</i><br /><searchLink fieldCode="AR" term="%22Perez-Guerrero%2C+Carmina%22">Perez-Guerrero, Carmina</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> carmina@eugenia.tech</i><br /><searchLink fieldCode="AR" term="%22Gonzalez-Mendoza%2C+Miguel%22">Gonzalez-Mendoza, Miguel</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mgonza@tec.mx</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Apr2025, Vol. 84 Issue 13, p11775-11794. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Motion+capture+%28Cinematography%29%22">Motion capture (Cinematography)</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Avatars+%28Virtual+reality%29%22">Avatars (Virtual reality)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Due to the increasing demand for virtual avatars, there has been a recent growth in the research and development of frameworks for realistic digital humans, which create a demand for realistic and adaptable facial motion capture systems. Most frameworks belong to private companies or represent high investments, which is why the creation of democratized solutions is relevant for the growth of digital human content creation. This research work proposes a facial motion capture framework for digital humans with the use of machine learning for facial codification intensity regression. The main focus is to use coded face movement intensities to generate realistic expressions on a digital human. The ablation studies performed on the regression models show that Neural Networks, using Histogram of Oriented Gradients as features, and with person-specific normalization, present overall better performance against other methods in the literature. With an RMSE of 0.052, the proposed framework offers reliable results that can be rendered in the face of a MetaHuman. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11042-024-19400-8 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 11775 Subjects: – SubjectFull: Motion capture (Cinematography) Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Image processing Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Avatars (Virtual reality) Type: general Titles: – TitleFull: Action unit intensity regression for facial MoCap aimed towards digital humans. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Vilchis, Carlos – PersonEntity: Name: NameFull: Mendez-Ruiz, Mauricio – PersonEntity: Name: NameFull: Perez-Guerrero, Carmina – PersonEntity: Name: NameFull: Gonzalez-Mendoza, Miguel IsPartOfRelationships: – BibEntity: Dates: – D: 22 M: 04 Text: Apr2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13807501 Numbering: – Type: volume Value: 84 – Type: issue Value: 13 Titles: – TitleFull: Multimedia Tools & Applications Type: main |
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