Abstract
Designing realistic multi-object scenes requires not only generating images, but also planning spatial layouts that respect semantic relations and physical plausibility. On one hand, while recent advances in diffusion models have enabled high-quality image generation, they lack explicit spatial reasoning, leading to unrealistic object layouts. On the other hand, traditional spatial planning methods in robotics emphasize geometric and relational consistency, but they struggle to capture semantic richness in visual scenes. To bridge this gap, in this paper, we propose LayoutAgent, an agentic framework that unifies vision-language reasoning with compositional diffusion for layout generation. Given multiple input images with target objects in them, our method first employs visual-language model to preprocess the inputs through segmentation, object size estimation, scene graph construction, and prompt rewriting. Then we leverage compositional diffusion-a method traditionally used in robotics-to synthesize bounding boxes that respect object relations encoded in the scene graph for spatial layouts. In the end, a foreground-conditioned image generator composes the complete scene by rendering the objects into the planned layout guided by designed prompts. Experiments demonstrate that LayoutAgent outperforms other state-of-the-art layout generation models in layout coherence, spatial realism and aesthetic alignment.
Abstract (translated)
设计逼真的多物体场景不仅需要生成图像，还需要规划符合语义关系和物理真实性的空间布局。一方面，尽管最近在扩散模型方面的进展已能够实现高质量的图像生成，但这些模型缺乏明确的空间推理能力，导致对象布局不现实。另一方面，传统的机器人领域中的空间规划方法强调几何和关联一致性，但在捕捉视觉场景中的语义丰富性方面存在困难。 为了解决这一问题，在本文中我们提出了LayoutAgent，这是一个将视觉-语言推理与组合扩散相结合的代理框架，用于生成布局。给定包含目标对象的多个输入图像后，我们的方法首先使用视觉-语言模型对这些输入进行预处理，包括分割、物体大小估计、场景图构建以及提示重写等步骤。然后，我们利用传统上在机器人领域中使用的组合扩散方法来合成符合场景图中的物体系数的空间布局边界框。最后，在设计的提示指导下，一个前景条件下的图像生成器通过将对象渲染到计划好的布局中来组成完整的场景。 实验结果表明，LayoutAgent在布局一致性、空间真实性和美学对齐方面优于其他最先进的布局生成模型。
URL
https://arxiv.org/abs/2509.22720
