PLay: Parametrically Conditioned Layout Generation using Latent Diffusion
This addresses the tedious manual effort in layout design for fields like UI and graphic design by providing intuitive user controls and improved performance.
The paper tackles the problem of automating layout design by introducing PLay, a conditional latent diffusion model that generates vector graphic layouts from user-specified guidelines, outperforming prior works on metrics like FID and FD-VG across three datasets.
Layout design is an important task in various design fields, including user interface, document, and graphic design. As this task requires tedious manual effort by designers, prior works have attempted to automate this process using generative models, but commonly fell short of providing intuitive user controls and achieving design objectives. In this paper, we build a conditional latent diffusion model, PLay, that generates parametrically conditioned layouts in vector graphic space from user-specified guidelines, which are commonly used by designers for representing their design intents in current practices. Our method outperforms prior works across three datasets on metrics including FID and FD-VG, and in user study. Moreover, it brings a novel and interactive experience to professional layout design processes.