LayoutGAN: Generating Graphic Layouts with Wireframe Discriminator

Layouts are important for graphic design and scene generation. We propose a novel generative adversarial network, named as LayoutGAN, that synthesizes graphic layouts by modeling semantic and geometric relations 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 semantic and geometric parameters jointly to produce a meaningful 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 visual domain. We validate the effectiveness of LayoutGAN in various experiments including MNIST digit generation, document layout generation, clipart abstract scene generation and tangram graphic design.

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