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Generating and manipulating human facial images using high-level attributalcontrols are important and interesting problems. The models proposed inprevious work can solve one of these two problems (generation or manipulation),but not both coherently. This paper proposes a novel model that learns how toboth 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) tosystematically learn relationship between user-defined and latent attributes,as well as between those attributes and RGB imagery. As part of this newlyformulated space, we propose a new model --- SL-GAN which is a specific form ofGenerative Adversarial Network. Finally, we present an iterative trainingalgorithm for SL-GAN. The experiments on recent CelebA and CASIA-WebFacedatasets validate the effectiveness of our proposed framework. We will alsomake data, pre-trained models and code available.

Semi-Latent GAN: Learning to generate and modify facial images from attributes