Abstract
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.
Highlights
Caricatures have been used for centuries to convey humor or sarcasm
In this paper we have introduced two novel approaches to automatically generate caricatures from 3D facial scans
We present and discuss a perceptual study aiming to assess the quality of the generated caricatures
Summary
Caricatures have been used for centuries to convey humor or sarcasm. References can be found during the Antiquity with Aristotle referring to these artists as “grotesque,” or in the works of Leonardo Da Vinci who was eagerly looking for people with deformities to use as models. Caricatures have been commonly used to entertain people, to laugh at politics or as a gift or souvenir sketched by street artists. These artists have the ability to capture distinct facial features, and exaggerate those features (Redman, 1984). With the development of social VR networks or games, users may wish to use stylized avatars, including avatars preserving their identity (Olivier et al, 2020) but with such exaggerated features Automatically generating such caricatured avatars becomes a key issue, as having artists manually creating caricatured avatars would not be feasible for such applications involving large numbers of users. The ability of creating a variety of plausible caricatures for each single face is a key challenge when automatically generating caricatures, as different artists would create visually different caricatures, which should be taken into account when evaluating the subjective quality of the results
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