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.

通过建立 LayoutGAN 生成对抗网络，用于建模不同类型的 2D 元素之间的几何关系以合成各种现实布局，同时采用自注意力机制和可微分线框图渲染层进行实现。验证实验包括 MNIST 数字生成、文档布局生成、剪纸抽象场景生成和七巧板图形设计。