  Facial expression synthesis has achieved remarkable advances with the advent
of Generative Adversarial Networks (GANs). However, GAN-based approaches mostly
generate photo-realistic results as long as the testing data distribution is
close to the training data distribution. The quality of GAN results
significantly degrades when testing images are from a slightly different
distribution. Moreover, recent work has shown that facial expressions can be
synthesized by changing localized face regions. In this work, we propose a
pixel-based facial expression synthesis method in which each output pixel
observes only one input pixel. The proposed method achieves good generalization
capability by leveraging only a few hundred training images. Experimental
results demonstrate that the proposed method performs comparably well against
state-of-the-art GANs on in-dataset images and significantly better on
out-of-dataset images. In addition, the proposed model is two orders of
magnitude smaller which makes it suitable for deployment on
resource-constrained devices.
