  Modeling layout is an important first step for graphic design. Recently,
methods for generating graphic layouts have progressed, particularly with
Generative Adversarial Networks (GANs). However, the problem of specifying the
locations and sizes of design elements usually involves constraints with
respect to element attributes, such as area, aspect ratio and reading-order.
Automating attribute conditional graphic layouts remains a complex and unsolved
problem. In this paper, we introduce Attribute-conditioned Layout GAN to
incorporate the attributes of design elements for graphic layout generation by
forcing both the generator and the discriminator to meet attribute conditions.
Due to the complexity of graphic designs, we further propose an element dropout
method to make the discriminator look at partial lists of elements and learn
their local patterns. In addition, we introduce various loss designs following
different design principles for layout optimization. We demonstrate that the
proposed method can synthesize graphic layouts conditioned on different element
attributes. It can also adjust well-designed layouts to new sizes while
retaining elements' original reading-orders. The effectiveness of our method is
validated through a user study.
