In this paper, we propose a Geometry-Contrastive Generative Adversarial
Network (GC-GAN) for transferring continuous emotions across different
subjects. Given an input face with certain emotion and a target facial
expression from another subject, GC-GAN can generate an identity-preserving
face with the target expression...
Geometry information is introduced into cGANs
as continuous conditions to guide the generation of facial expressions. In
order to handle the misalignment across different subjects or emotions,
contrastive learning is used to transform geometry manifold into an embedded
semantic manifold of facial expressions. Therefore, the embedded geometry is
injected into the latent space of GANs and control the emotion generation
effectively. Experimental results demonstrate that our proposed method can be
applied in facial expression transfer even there exist big differences in
facial shapes and expressions between different subjects.