Premium
Emotional facial expression transfer from a single image via generative adversarial nets
Author(s) -
Qiao Fengchun,
Yao Naiming,
Jiao Zirui,
Li Zhihao,
Chen Hui,
Wang Hongan
Publication year - 2018
Publication title -
computer animation and virtual worlds
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 49
eISSN - 1546-427X
pISSN - 1546-4261
DOI - 10.1002/cav.1819
Subject(s) - computer science , facial expression , artificial intelligence , expression (computer science) , generative grammar , face (sociological concept) , computer graphics , computer vision , image (mathematics) , generative model , sequence (biology) , pattern recognition (psychology) , social science , sociology , biology , genetics , programming language
Abstract 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.