  Layout is important for graphic design and scene generation. We propose a
novel Generative Adversarial Network, called LayoutGAN, that synthesizes
layouts by modeling geometric relations of different types of 2D elements. The
generator of LayoutGAN takes as input a set of randomly-placed 2D graphic
elements and uses self-attention modules to refine their labels and geometric
parameters jointly to produce a realistic layout. Accurate alignment is
critical for good layouts. We thus propose a novel differentiable wireframe
rendering layer that maps the generated layout to a wireframe image, upon which
a CNN-based discriminator is used to optimize the layouts in image space. We
validate the effectiveness of LayoutGAN in various experiments including MNIST
digit generation, document layout generation, clipart abstract scene generation
and tangram graphic design.
