Facial Expression Transfer Model Based on Conditional Generative Adversarial Network
摘要
Facial expression transfer is a key technology for character animation in computer vision,but existing facial expression transfer methods have some problems,such as unnatural expression generation,lack of realism,complex transfer model and difficulty in training.Therefore,a face expression transfer model based on conditional Generative Adversarial Network(GAN) is constructed.The condition of the expression domain is specified by the classification loss function of the design domain,so that a single generator can learn the mapping relations between multiple expression domains.Meanwhile,the conditional constraints and zero-sum game between the model generator and the discriminator are used to realize the transfer of five facial expressions by training only one generator.Experimental results show that,this model can effectively transfer facial expressions and has strong robustness.Facial expressions generated by the proposed model are more natural and realistic than the StarGAN model.