This paper explores the task of Complex Visual Text Generation (CVTG), which centers on generating intricate textual content distributed across diverse regions within visual images. In CVTG, image generation models often rendering distorted and blurred visual text or missing some visual text. To tackle these challenges, we propose TextCrafter, a novel multi-visual text rendering method. TextCrafter employs a progressive strategy to decompose complex visual text into distinct components while ensuring robust alignment between textual content and its visual carrier. Additionally, it incorporates a token focus enhancement mechanism to amplify the prominence of visual text during the generation process. TextCrafter effectively addresses key challenges in CVTG tasks, such as text confusion, omissions, and blurriness. Moreover, we present a new benchmark dataset, CVTG-2K, tailored to rigorously evaluate the performance of generative models on CVTG tasks. Extensive experiments demonstrate that our method surpasses state-of-the-art approaches.

翻译：本文探讨了复杂视觉文本生成（CVTG）任务，该任务的核心在于生成分布于视觉图像内不同区域的复杂文本内容。在CVTG中，图像生成模型常出现渲染出的视觉文本扭曲、模糊或缺失部分视觉文本的问题。为应对这些挑战，我们提出了TextCrafter，一种新颖的多视觉文本渲染方法。TextCrafter采用渐进式策略，将复杂视觉文本分解为不同组成部分，同时确保文本内容与其视觉载体之间的强健对齐。此外，该方法引入了令牌聚焦增强机制，以在生成过程中提升视觉文本的显著度。TextCrafter有效解决了CVTG任务中的关键挑战，如文本混淆、遗漏和模糊。此外，我们提出了一个新的基准数据集CVTG-2K，专为严格评估生成模型在CVTG任务上的性能而设计。大量实验表明，我们的方法超越了现有最先进的方法。