Basis Restricted Elastic Shape Analysis on the Space of Unregistered Surfaces.
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| Title: | Basis Restricted Elastic Shape Analysis on the Space of Unregistered Surfaces. |
|---|---|
| Authors: | Hartman, Emmanuel1 (AUTHOR) ehartman@math.fsu.edu, Pierson, Emery2 (AUTHOR) emery.pierson@courrier.dev, Bauer, Martin1,3 (AUTHOR) bauer@math.fsu.edu, Daoudi, Mohamed4,5 (AUTHOR) mohamed.daoudi@imt-nord-europe.fr, Charon, Nicolas6 (AUTHOR) ncharon@central.uh.edu |
| Source: | International Journal of Computer Vision. Apr2025, Vol. 133 Issue 4, p1999-2024. 26p. |
| Subjects: | Riemannian metric, Elastic analysis (Engineering), Geometric analysis, Deep learning, Human body |
| Abstract: | This paper introduces a new framework for surface analysis derived from the general setting of elastic Riemannian metrics on shape spaces. Traditionally, those metrics are defined over the infinite dimensional manifold of immersed surfaces and satisfy specific invariance properties enabling the comparison of surfaces modulo shape preserving transformations such as reparametrizations. The specificity of our approach is to restrict the space of allowable transformations to predefined finite dimensional bases of deformation fields. These are estimated in a data-driven way so as to emulate specific types of surface transformations. This allows us to simplify the representation of the corresponding shape space to a finite dimensional latent space. However, in sharp contrast with methods involving e.g. mesh autoencoders, the latent space is equipped with a non-Euclidean Riemannian metric inherited from the family of elastic metrics. We demonstrate how this model can be effectively implemented to perform a variety of tasks on surface meshes which, importantly, does not assume these to be pre-registered or to even have a consistent mesh structure. We specifically validate our approach on human body shape and pose data as well as human face and hand scans for problems such as shape registration, interpolation, motion transfer or random pose generation. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Computer Vision 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: 184168312 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Basis Restricted Elastic Shape Analysis on the Space of Unregistered Surfaces. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hartman%2C+Emmanuel%22">Hartman, Emmanuel</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ehartman@math.fsu.edu</i><br /><searchLink fieldCode="AR" term="%22Pierson%2C+Emery%22">Pierson, Emery</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> emery.pierson@courrier.dev</i><br /><searchLink fieldCode="AR" term="%22Bauer%2C+Martin%22">Bauer, Martin</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> bauer@math.fsu.edu</i><br /><searchLink fieldCode="AR" term="%22Daoudi%2C+Mohamed%22">Daoudi, Mohamed</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<i> mohamed.daoudi@imt-nord-europe.fr</i><br /><searchLink fieldCode="AR" term="%22Charon%2C+Nicolas%22">Charon, Nicolas</searchLink><relatesTo>6</relatesTo> (AUTHOR)<i> ncharon@central.uh.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Computer+Vision%22">International Journal of Computer Vision</searchLink>. Apr2025, Vol. 133 Issue 4, p1999-2024. 26p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Riemannian+metric%22">Riemannian metric</searchLink><br /><searchLink fieldCode="DE" term="%22Elastic+analysis+%28Engineering%29%22">Elastic analysis (Engineering)</searchLink><br /><searchLink fieldCode="DE" term="%22Geometric+analysis%22">Geometric analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Human+body%22">Human body</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper introduces a new framework for surface analysis derived from the general setting of elastic Riemannian metrics on shape spaces. Traditionally, those metrics are defined over the infinite dimensional manifold of immersed surfaces and satisfy specific invariance properties enabling the comparison of surfaces modulo shape preserving transformations such as reparametrizations. The specificity of our approach is to restrict the space of allowable transformations to predefined finite dimensional bases of deformation fields. These are estimated in a data-driven way so as to emulate specific types of surface transformations. This allows us to simplify the representation of the corresponding shape space to a finite dimensional latent space. However, in sharp contrast with methods involving e.g. mesh autoencoders, the latent space is equipped with a non-Euclidean Riemannian metric inherited from the family of elastic metrics. We demonstrate how this model can be effectively implemented to perform a variety of tasks on surface meshes which, importantly, does not assume these to be pre-registered or to even have a consistent mesh structure. We specifically validate our approach on human body shape and pose data as well as human face and hand scans for problems such as shape registration, interpolation, motion transfer or random pose generation. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Computer Vision 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/s11263-024-02269-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 1999 Subjects: – SubjectFull: Riemannian metric Type: general – SubjectFull: Elastic analysis (Engineering) Type: general – SubjectFull: Geometric analysis Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Human body Type: general Titles: – TitleFull: Basis Restricted Elastic Shape Analysis on the Space of Unregistered Surfaces. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hartman, Emmanuel – PersonEntity: Name: NameFull: Pierson, Emery – PersonEntity: Name: NameFull: Bauer, Martin – PersonEntity: Name: NameFull: Daoudi, Mohamed – PersonEntity: Name: NameFull: Charon, Nicolas IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09205691 Numbering: – Type: volume Value: 133 – Type: issue Value: 4 Titles: – TitleFull: International Journal of Computer Vision Type: main |
| ResultId | 1 |
