Learning Distribution Independent Latent Representation for 3D Face Disentanglement

Abstract

Learning disentangled 3D face shape representation is beneficial to face attribute transfer, generation and recognition, etc. In this paper, we propose a novel distribution independence-based method to learn to decompose 3D face shapes. Specifically, we design a variational auto-encoder with Graph Convolutional Network (GCN), namely Mesh-Encoder, to model the distributions of identity and expression representations via variational inference. To disentangle facial expression and identity, we eliminate correlation of the two distributions, and enforce them to be independent by adversarial training. Extensive experiments show that the proposed approach can achieve state-of-the-art results in 3D face shape decomposition and expression transfer. Though focusing on disentanglement, our method also achieves the reconstruction accuracies comparable to the state-of-the-art 3D face reconstruction methods.