ContentDM: A Layout Diffusion Model for Content-Aware Layout Generation

Abstract

Content-aware layout generation aims to produce fitting element arrangements based on the background contents, which is used for graphic design applications such as automatic poster layout design. In this article, we propose ContentDM, a layout diffusion model specifically designed for the content-aware layout generation task, overcoming the limitations suffered from existing methods: irrational arrangement among layout elements and lack of refining ability for coarse generated results. ContentDM defines the layout diffusion process through random perturbations applied to both the position and type of layout elements. During the denoising training phase, the content-aware layout generator is trained to reconstruct samples from these perturbed layouts. This process enables the model to learn the correct arrangement patterns within the layout elements, thereby enhancing the rationality of generated layouts. Moreover, we develop an iterative layout inference strategy to enable the layout generator to refine the generated layouts progressively, thereby enhancing the overall quality of the generation results. Extensive experiments demonstrate that ContentDM significantly outperforms existing methods, achieving state-of-the-art performance in content-aware layout generation, both in terms of visual quality and quantitative metrics.