  Generating and manipulating human facial images using high-level attributal
controls are important and interesting problems. The models proposed in
previous work can solve one of these two problems (generation or manipulation),
but not both coherently. This paper proposes a novel model that learns how to
both generate and modify the facial image from high-level semantic attributes.
Our key idea is to formulate a Semi-Latent Facial Attribute Space (SL-FAS) to
systematically learn relationship between user-defined and latent attributes,
as well as between those attributes and RGB imagery. As part of this newly
formulated space, we propose a new model --- SL-GAN which is a specific form of
Generative Adversarial Network. Finally, we present an iterative training
algorithm for SL-GAN. The experiments on recent CelebA and CASIA-WebFace
datasets validate the effectiveness of our proposed framework. We will also
make data, pre-trained models and code available.
