  Generative models able to synthesize layouts of different kinds (e.g.
documents, user interfaces or furniture arrangements) are a useful tool to aid
design processes and as a first step in the generation of synthetic data, among
other tasks. We exploit the properties of self-attention layers to capture high
level relationships between elements in a layout, and use these as the building
blocks of the well-known Variational Autoencoder (VAE) formulation. Our
proposed Variational Transformer Network (VTN) is capable of learning margins,
alignments and other global design rules without explicit supervision. Layouts
sampled from our model have a high degree of resemblance to the training data,
while demonstrating appealing diversity. In an extensive evaluation on publicly
available benchmarks for different layout types VTNs achieve state-of-the-art
diversity and perceptual quality. Additionally, we show the capabilities of
this method as part of a document layout detection pipeline.
