  We propose a novel framework for simultaneously generating and manipulating
the face images with desired attributes. While the state-of-the-art attribute
editing technique has achieved the impressive performance for creating
realistic attribute effects, they only address the image editing problem, using
the input image as the condition of model. Recently, several studies attempt to
tackle both novel face generation and attribute editing problem using a single
solution. However, their image quality is still unsatisfactory. Our goal is to
develop a single unified model that can simultaneously create and edit high
quality face images with desired attributes. A key idea of our work is that we
decompose the image into the latent and attribute vector in low dimensional
representation, and then utilize the GAN framework for mapping the low
dimensional representation to the image. In this way, we can address both the
generation and editing problem by learning the generator. For qualitative and
quantitative evaluations, the proposed algorithm outperforms recent algorithms
addressing the same problem. Also, we show that our model can achieve the
competitive performance with the state-of-the-art attribute editing technique
in terms of attribute editing quality.
