ReLayout: Integrating Relation Reasoning for Content-aware Layout Generation with Multi-modal Large Language Models
This work addresses layout generation for design applications, but it is incremental as it builds on existing LLM-based methods by adding relation reasoning.
The paper tackles the problem of content-aware layout generation by addressing the failure of existing LLM-based methods to adequately interpret spatial relationships, leading to structural and diversity issues. The result is that ReLayout outperforms baselines, generating more structured, diverse, and aesthetically coherent layouts.
Content-aware layout aims to arrange design elements appropriately on a given canvas to convey information effectively. Recently, the trend for this task has been to leverage large language models (LLMs) to generate layouts automatically, achieving remarkable performance. However, existing LLM-based methods fail to adequately interpret spatial relationships among visual themes and design elements, leading to structural and diverse problems in layout generation. To address this issue, we introduce ReLayout, a novel method that leverages relation-CoT to generate more reasonable and aesthetically coherent layouts by fundamentally originating from design concepts. Specifically, we enhance layout annotations by introducing explicit relation definitions, such as region, salient, and margin between elements, with the goal of decomposing the layout into smaller, structured, and recursive layouts, thereby enabling the generation of more structured layouts. Furthermore, based on these defined relationships, we introduce a layout prototype rebalance sampler, which defines layout prototype features across three dimensions and quantifies distinct layout styles. This sampler addresses uniformity issues in generation that arise from data bias in the prototype distribution balance process. Extensive experimental results verify that ReLayout outperforms baselines and can generate structural and diverse layouts that are more aligned with human aesthetics and more explainable.