Poster layout is a crucial aspect of poster design. Prior methods primarily focus on the correlation between visual content and graphic elements. However, a pleasant layout should also consider the relationship between visual and textual contents and the relationship between elements. In this study, we introduce a relation-aware diffusion model for poster layout generation that incorporates these two relationships in the generation process. Firstly, we devise a visual-textual relation-aware module that aligns the visual and textual representations across modalities, thereby enhancing the layout's efficacy in conveying textual information. Subsequently, we propose a geometry relation-aware module that learns the geometry relationship between elements by comprehensively considering contextual information. Additionally, the proposed method can generate diverse layouts based on user constraints. To advance research in this field, we have constructed a poster layout dataset named CGL-Dataset V2. Our proposed method outperforms state-of-the-art methods on CGL-Dataset V2. The data and code will be available at https://github.com/liuan0803/RADM.

翻译：海报布局是海报设计的关键方面。现有方法主要关注视觉内容与图形元素之间的相关性。然而，一个舒适的布局还应考虑视觉内容与文本内容之间的关系以及元素间的关系。本研究提出一种面向关系感知的海报布局生成扩散模型，在生成过程中融合了这两种关系。首先，我们设计了一个视觉-文本关系感知模块，该模块跨模态对齐视觉和文本表示，从而增强布局在传达文本信息方面的有效性。其次，我们提出一个几何关系感知模块，通过全面考虑上下文信息来学习元素之间的几何关系。此外，所提方法能够基于用户约束生成多样化布局。为推进该领域研究，我们构建了名为CGL-Dataset V2的海报布局数据集。实验表明，所提方法在CGL-Dataset V2上优于现有最优方法。相关数据和代码将在 https://github.com/liuan0803/RADM 公开。