  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.
