用生成模型优化图文布局，让文字更清晰且不破坏背景
Neural Contrast: Leveraging Generative Editing for Graphic Design Recommendations
- 用扩散模型生成低显著性区域以增强图文对比
- 在花纹背景上仍能保持文字高可见性
- 适合需要高质量图文排版的设计师或AI工具开发者
创作视觉吸引人的合成图像需优化文本与背景的兼容性。以往方法多采用简单设计策略，如更改文字颜色或添加背景形状以提升对比度，但这些方式常具破坏性，会改变文字颜色或部分遮挡背景图像。另一方法是将设计元素置于低显著性且对比明显的区域，但在纹理复杂的背景下效果不佳。为此，我们提出一种基于扩散模型的生成式方法，确保设计元素下方区域具有低显著性同时增强对比度，从而提升设计元素的可见性。
原文摘要 · Abstract (English)
Creating visually appealing composites requires optimizing both text and background for compatibility. Previous methods have focused on simple design strategies, such as changing text color or adding background shapes for contrast. These approaches are often destructive, altering text color or partially obstructing the background image. Another method involves placing design elements in non-salient and contrasting regions, but this isn't always effective, especially with patterned backgrounds. To address these challenges, we propose a generative approach using a diffusion model. This method ensures the altered regions beneath design assets exhibit low saliency while enhancing contrast, thereby improving the visibility of the design asset.
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