  Facial expression generation has always been an intriguing task for
scientists and researchers all over the globe. In this context, we present our
novel approach for generating videos of the six basic facial expressions.
Starting from a single neutral facial image and a label indicating the desired
facial expression, we aim to synthesize a video of the given identity
performing the specified facial expression. Our approach, referred to as
FEV-GAN (Facial Expression Video GAN), is based on Spatio-temporal
Convolutional GANs, that are known to model both content and motion in the same
network. Previous methods based on such a network have shown a good ability to
generate coherent videos with smooth temporal evolution. However, they still
suffer from low image quality and low identity preservation capability. In this
work, we address this problem by using a generator composed of two image
encoders. The first one is pre-trained for facial identity feature extraction
and the second for spatial feature extraction. We have qualitatively and
quantitatively evaluated our model on two international facial expression
benchmark databases: MUG and Oulu-CASIA NIR&VIS. The experimental results
analysis demonstrates the effectiveness of our approach in generating videos of
the six basic facial expressions while preserving the input identity. The
analysis also proves that the use of both identity and spatial features
enhances the decoder ability to better preserve the identity and generate
high-quality videos. The code and the pre-trained model will soon be made
publicly available.
