Emotional facial expression transfer from a single image via generative adversarial nets

• Published in: Computer animation and virtual worlds Vol. 29; no. 3-4
• Main Authors: Qiao, Fengchun, Yao, Naiming, Jiao, Zirui, Li, Zhihao, Chen, Hui, Wang, Hongan
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Subscription Services, Inc 01.05.2018
• Subjects: Computer graphics; Computer vision; dynamic expression sequence; facial expression transfer; generative adversarial nets; Image quality; Landmarks; Target recognition
• ISSN: 1546-4261; 1546-427X
• DOI: 10.1002/cav.1819

Facial expression transfer from a single image is a challenging task and has drawn sustained attention in the fields of computer vision and computer graphics. Recently, generative adversarial nets (GANs) have provided a new approach to facial expression transfer from a single image toward target facial expressions. However, it is still difficult to obtain a sequence of smoothly changed facial expressions. We present a novel GAN‐based method for generating emotional facial expression animations given a single image and several facial landmarks for the in‐between stages. In particular, landmarks of other subjects are incorporated into a GAN model to control the generated facial expression from a latent space. With the trained model, high‐quality face images and a smoothly changed facial expression sequence can be effectively obtained, which are showed qualitatively and quantitatively in our experiments on the Multi‐PIE and CK+ data sets. We present a novel GAN‐based method for generating emotional facial expression animations given a single image and several facial landmarks for the in‐between stages. With the trained model, high‐quality face images and smoothly‐changed facial expression sequence can be effectively obtained.