Two-stage Content-Aware Layout Generation for Poster Designs

Abstract

Automatic layout generation models can generate numerous design layouts in a few seconds, which significantly reduces the amount of repetitive work for designers. However, most of these models consider the layout generation task as arranging layout elements with different attributes on a blank canvas, thus struggle to handle the case when an image is used as the layout background. Additionally, existing layout generation models often fail to incorporate explicit aesthetic principles such as alignment and non-overlap, and neglect implicit aesthetic principles which are hard to model. To address these issues, this paper proposes a two-stage content-aware layout generation framework for poster layout generation. Our framework consists of an aesthetics-conditioned layout generation module and a layout ranking module. The diffusion model based layout generation module utilizes an aesthetics-guided layout denoising process to sample layout proposals that meet explicit aesthetic constraints. The Auto-Encoder based layout ranking module then measures the distance between those proposals and real designs to determine the layout that best meets implicit aesthetic principles. Quantitative and qualitative experiments demonstrate that our method outperforms state-of-the-art content-aware layout generation models.