TextCrafter: Accurately Rendering Multiple Texts in Complex Visual
  Scenes
Authors: name: Nikai Du, name: Zhennan Chen, name: Zhizhou Chen, name: Shan Gao, name: Xi Chen, name: Zhengkai Jiang, name: Jian Yang, name: Ying Tai

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.