Metadata only
Date
2021Type
- Conference Paper
ETH Bibliography
yes
Altmetrics
Abstract
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. Show more
Publication status
publishedExternal links
Book title
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Pages / Article No.
Publisher
IEEEEvent
More
Show all metadata
ETH Bibliography
yes
Altmetrics