Geometry-Contrastive Generative Adversarial Network for Facial Expression Synthesis

In this paper, we propose a geometry-contrastive generative adversarial network GC-GAN for generating facial expression images conditioned on geometry information. Specifically, given an input face and a target expression designated by a set of facial landmarks, an identity-preserving face can be generated guided by the target expression. In order to embed facial geometry onto a semantic manifold, we incorporate contrastive learning into conditional GANs. Experiment results demonstrate that the manifold is sensitive to the changes of facial geometry both globally and locally. Benefited from the semantic manifold, dynamic smooth transitions between different facial expressions are exhibited via geometry interpolation. Furthermore, our method can also be applied in facial expression transfer even there exist big differences in face shape between target faces and driving faces.