Even if the appearance and geometry of the human eye have been extensively studied during the last decade, the geometrical correlation between gaze direction, eyelids aperture and eyelids shape has not been empirically modeled. In this paper, we propose a data‐driven approach for capturing and modeling the subtle features of the human eye region, such as the inner eye corner and the skin bulging effect due to globe orientation. Our approach consists of an original experimental setup to capture the eye region geometry variations combined with a 3D reconstruction method. Regarding the eye region capture, we scanned 55 participants doing 36 eyes poses. To animate a participant's eye region, we register the different poses to a vertex wise correspondence before blending them in a trilinear fashion. We show that our 3D animation results are visually pleasant and realistic while bringing novel eye features compared to state of the art models.

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.