  Face synthesis has been a fascinating yet challenging problem in computer
vision and machine learning. Its main research effort is to design algorithms
to generate photo-realistic face images via given semantic domain. It has been
a crucial prepossessing step of main-stream face recognition approaches and an
excellent test of AI ability to use complicated probability distributions. In
this paper, we provide a comprehensive review of typical face synthesis works
that involve traditional methods as well as advanced deep learning approaches.
Particularly, Generative Adversarial Net (GAN) is highlighted to generate
photo-realistic and identity preserving results. Furthermore, the public
available databases and evaluation metrics are introduced in details. We end
the review with discussing unsolved difficulties and promising directions for
future research.
