Geometry-Contrastive GAN for Facial Expression Transfer

Understand

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.