Learning a crowd-powered perceptual distance metric for facial blendshapes

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dc.contributor.authorCipiloglu Yildiz Z.
dc.date.accessioned2024-07-22T08:02:02Z
dc.date.available2024-07-22T08:02:02Z
dc.date.issued2023
dc.description.abstractIt is known that purely geometric distance metrics cannot reflect the human perception of facial expressions. A novel perceptually based distance metric designed for 3D facial blendshape models is proposed in this paper. To develop this metric, comparative evaluations of facial expressions were collected from a crowdsourcing experiment. Then, the weights of a distance metric, based on descriptive features of the models, were optimized to match the results with crowdsourced data, through a metric learning process. The method incorporates perceptual properties such as curvature and visual saliency. A formal analysis of the results proves the high correlation between the metric output and human perception. The effectiveness and success of the proposed metric were also compared to other distance alternatives. The proposed metric will enable intelligent processing of 3D facial blendshapes data in several ways. It will be possible to generate perceptually valid clustering and visualization of 3D facial blendshapes. It will help reduce storage and computational requirements by removing redundant expressions that are perceptually identical from the overall dataset. It can also be used to assist novice animators while creating plausible and expressive facial animations. © 2023, The Author(s).
dc.identifier.DOI-ID10.1186/s13640-023-00609-w
dc.identifier.issn16875176
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/11676
dc.language.isoEnglish
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.rightsAll Open Access; Gold Open Access
dc.subjectAnimation
dc.subjectDigital storage
dc.subjectLearning systems
dc.subjectThree dimensional computer graphics
dc.subjectVisualization
dc.subjectBlendshapes
dc.subjectComparative evaluations
dc.subjectDistance metrics
dc.subjectFacial Expressions
dc.subjectGeometric distances
dc.subjectHuman perception
dc.subjectLearning process
dc.subjectMetric learning
dc.subjectPerceptual distance
dc.subjectVisual perception
dc.subjectCrowdsourcing
dc.titleLearning a crowd-powered perceptual distance metric for facial blendshapes
dc.typeArticle

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