Perceptual Caricaturization of 3D Models

Cimen, Gokcen; Bulbul, Abdullah; Özgüç, Bülent; Çapın, Tolga

doi:10.1007/978-1-4471-4594-3_21

Cited by 3 publications

(3 citation statements)

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“…With no reference model, their method can enhance or reduce the sharpness of a 3D shape. The link between saliency and caricature has been explored by Cimen et al (2012). They introduced a perceptual method for caricaturing 3D shapes based on their saliency using free form deformation technique.…”

Section: Rule-based Methodsmentioning

confidence: 99%

“…General shape rule-based methods can also caricature a 2D or 3D shape without any reference model. As they do not take into account any statistical information nor the concept of artistic style, they try to link low-level geometry information to high-level caricature concepts, e.g., the fact that the most salient area should be more exaggerated (Cimen et al, 2012). As a result, they do not take into account the semantic of faces nor the art of human face caricature.…”

Section: Rule-based Methodsmentioning

confidence: 99%

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Study on Automatic 3D Facial Caricaturization: From Rules to Deep Learning

Olivier

Kerbiriou

Arguelaguet

et al. 2022

Front. Virtual Real.

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Facial caricature is the art of drawing faces in an exaggerated way to convey emotions such as humor or sarcasm. Automatic caricaturization has been explored both in the 2D and 3D domain. In this paper, we propose two novel approaches to automatically caricaturize input facial scans, filling gaps in the literature in terms of user-control, caricature style transfer, and exploring the use of deep learning for 3D mesh caricaturization. The first approach is a gradient-based differential deformation approach with data driven stylization. It is a combination of two deformation processes: facial curvature and proportions exaggeration. The second approach is a GAN for unpaired face-scan-to-3D-caricature translation. We leverage existing facial and caricature datasets, along with recent domain-to-domain translation methods and 3D convolutional operators, to learn to caricaturize 3D facial scans in an unsupervised way. To evaluate and compare these two novel approaches with the state of the art, we conducted the first user study of facial mesh caricaturization techniques, with 49 participants. It highlights the subjectivity of the caricature perception and the complementarity of the methods. Finally, we provide insights for automatically generating caricaturized 3D facial mesh.

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Section: Rule-based Methodsmentioning

confidence: 99%

Section: Rule-based Methodsmentioning

confidence: 99%