Desigen：可控设计模板生成流水线
Desigen: A Pipeline for Controllable Design Template Generation
AI总结：
本文提出Desigen流水线，通过约束显著度与迭代推理生成背景及和谐布局，自动创建高质量设计模板，实验证明其质量堪比人类设计师。
摘要：
模板是实施设计（如横幅、幻灯片）的良好起点，但设计师手动创建模板需要付出巨大努力。在本文中，我们提出了Desigen，一种自动模板创建流水线，它生成背景图像以及背景上和谐布局元素。与自然图像不同，背景图像应为叠加的布局元素保留足够的非显著空间。为了使现有先进的基于扩散的模型具备更强的空间控制能力，我们提出了两种简单但有效的技术，在背景生成过程中约束显著度分布并降低期望区域的注意力权重。然后，以背景为条件，我们使用基于Transformer的自回归生成器合成布局。为了实现更和谐的组合，我们提出了一种迭代推理策略，在多个轮次中调整合成的背景和布局。我们构建了一个包含超过4万个广告横幅的设计数据集来验证我们的方法。大量实验表明，所提出的流水线生成的模板质量可与人类设计师相媲美。除了单页设计，我们进一步展示了演示文稿生成的应用，该应用输出一组主题一致的幻灯片。数据和代码可在https://whaohan.github.io/desigen获取。
英文摘要：
Templates serve as a good starting point to implement a design (e.g., banner, slide) but it takes great effort from designers to manually create. In this paper, we present Desigen, an automatic template creation pipeline which generates background images as well as harmonious layout elements over the background. Different from natural images, a background image should preserve enough non-salient space for the overlaying layout elements. To equip existing advanced diffusion-based models with stronger spatial control, we propose two simple but effective techniques to constrain the saliency distribution and reduce the attention weight in desired regions during the background generation process. Then conditioned on the background, we synthesize the layout with a Transformer-based autoregressive generator. To achieve a more harmonious composition, we propose an iterative inference strategy to adjust the synthesized background and layout in multiple rounds. We constructed a design dataset with more than 40k advertisement banners to verify our approach. Extensive experiments demonstrate that the proposed pipeline generates high-quality templates comparable to human designers. More than a single-page design, we further show an application of presentation generation that outputs a set of theme-consistent slides. The data and code are available at https://whaohan.github.io/desigen.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
