Interactive modeling of 3D facial expressions with hierarchical Gaussian process latent variable models

The natural expressions play an important role in the daily communication. The efficient and intuitive facial expression editing based on the limited constraints is desirable in the facial animation. In this paper, we present an interactive 3D facial expression editing system with the hierarchical Gaussian process latent variable model (HGPLVM). The hierarchical model incorporates the joint work of the local facial features to produce the natural expressions. To deal with the holistic expression modeling from the local constraints, the inverse mapping between the low-level feature nodes and the high-level facial region nodes is established by the RBF regression model in the latent space. A propagation algorithm is introduced to predict the holistic facial configurations. The experiments demonstrate the 3D facial expressions satisfying the user constraints can be produced efficiently.