title: TextCrafter: Accurately Rendering Multiple Texts in Complex Visual
    Scenes
summary: 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.
paper_authors._id: 67eb594988a08fae617242f1
paper_authors.name: Nikai Du
paper_authors._id: 67eb594988a08fae617242f2
paper_authors.name: Zhennan Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-01T07:46:46.364Z
paper_authors._id: 66449e619ff401732687f013
paper_authors.avatarUrl: /avatars/251897d1324a70a9bf761513871c5841.svg
paper_authors.fullname: chen
paper_authors.type: user
paper_authors.user: zhen-nan
paper_authors._id: 67eb594988a08fae617242f3
paper_authors.name: Zhizhou Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-01T07:57:44.605Z
paper_authors._id: 637c22183d8e2e9c40c09fcf
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1669079538761-noauth.jpeg
paper_authors.fullname: Zhizhou Chen
paper_authors.type: user
paper_authors.user: Chenzzzzzz
paper_authors._id: 67eb594988a08fae617242f4
paper_authors.name: Shan Gao
paper_authors._id: 67eb594988a08fae617242f5
paper_authors.name: Xi Chen
paper_authors._id: 67eb594988a08fae617242f6
paper_authors.name: Zhengkai Jiang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-01T07:58:12.614Z
paper_authors._id: 67593dd0f522f4409e614ba0
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/67593dd0f522f4409e614ba0/cvb9w_8seu3Kbjg_XAnNj.jpeg
paper_authors.fullname: Jiang Zhengkai
paper_authors.type: user
paper_authors.user: jzzzzk
paper_authors._id: 67eb594988a08fae617242f7
paper_authors.name: Jian Yang
paper_authors._id: 67eb594988a08fae617242f8
paper_authors.name: Ying Tai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-01T07:46:44.350Z
paper_authors._id: 65734004769f3ee9bde1af10
paper_authors.avatarUrl: /avatars/d6310ed861972fd691687d8f47413f33.svg
paper_authors.fullname: Ying Tai
paper_authors.type: user
paper_authors.user: yingtai
paper_title: TextCrafter: Accurately Rendering Multiple Texts in Complex Visual
    Scenes
paper_summary: 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.
paper_ai_summary: TextCrafter is a progressive method that enhances the generation of intricate visual text within images, addressing issues like distortion, blurriness, and omissions through token focus enhancement.
paper_ai_keywords: Complex Visual Text Generation (CVTG) multi-visual text rendering token focus enhancement CVTG-2K