Adversarial 3D Face Disentanglement of Identity and Expression

We propose a new framework to decompose 3D facial shape into identity and expression. Existing 3D face disentanglement methods assume the presence of a corresponding neutral (i.e. identity) face for each subject. Our method designs an identity discriminator to obviate this requirement. This is a binary classifier that determines if two input faces are from the same identity, and encourages the synthesised identity face to have the same identity features as the input face and to approach the 'apathy' expression. To this end, we take advantage of adversarial learning to train a PointNet-based variational auto-encoder and discriminator. Comprehensive experiments are employed on CoMA, BU3DFE, and FaceScape datasets. Results demonstrate state-of-the-art performance with the option of operating in a more versatile application setting of no known neutral ground truths. Code is available at https://github.com/rmraaron/FaceExpDisentanglement.