A distribution independence based method for 3D face shape decomposition

Decomposing a 3D face shape into different attribute components is usually beneficial to many applications, such as 3D face generation and attribute transfer. In this paper, we propose a novel method to learn independent latent representations of 3D face shapes to decompose a given 3D face shape into identity and expression components. We assume that the identity describes the intrinsic geometry of a face while the expression captures the extrinsic one, and thus they are independent of each other. Based on this assumption, we encode a 3D face shape into its identity and expression representations by a variational inference framework, that is equipped with Graph Convolutional Networks (GCN). Furthermore, we introduce a binary discriminator to enforce the latent representations of identity and expression to be distribution independent by adversarial learning. Both qualitative and quantitative experimental results show that the proposed approach can achieve state-of-the-art results in 3D face shape decomposition. Extensive applications on 3D facial expression transfer, 3D face recognition, and 3D face generation further demonstrate that the proposed method can achieve visually better transferred expressions, purer identity representations, and more diverse 3D face shapes, compared with existing state-of-the-art methods.