Landmark-Guided Deformation Transfer of Template Facial Expressions for Automatic Generation of Avatar Blendshapes

Abstract

Blendshape models are commonly used to track and re-target facial expressions to virtual avatars using RGB-D cameras and without using any facial marker. When using blendshape models, the target avatar model must possess a set of key-shapes that can be blended depending on the estimated facial expression. Creating realistic set of key-shapes is extremely difficult and requires time and professional expertise. As a consequence, blendshape-based re-targeting technology can only be used with a limited amount of pre-built avatar models, which is not attractive for the large public. In this paper, we propose an automatic method to easily generate realistic key-shapes of any avatar that map directly to the source blendshape model (the user is only required to select a few facial landmarks on the avatar mesh). By doing so, captured facial motion can be easily re-targeted to any avatar, even when the avatar has largely different shape and topology compared with the source template mesh. Our experimental results show the accuracy of our proposed method compared with the state-of-the-art method for mesh deformation transfer.