TextCrafter: Accurately Rendering Multiple Texts in Complex Visual
  Scenes
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.
67eb594988a08fae617242f1
Nikai Du
67eb594988a08fae617242f2
Zhennan Chen
claimed_verified
2025-04-01T07:46:46.364Z
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chen
user
zhen-nan
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Zhizhou Chen
admin_assigned
2025-04-01T07:57:44.605Z
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Zhizhou Chen
user
Chenzzzzzz
67eb594988a08fae617242f4
Shan Gao
67eb594988a08fae617242f5
Xi Chen
67eb594988a08fae617242f6
Zhengkai Jiang
admin_assigned
2025-04-01T07:58:12.614Z
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Jiang Zhengkai
user
jzzzzk
67eb594988a08fae617242f7
Jian Yang
67eb594988a08fae617242f8
Ying Tai
claimed_verified
2025-04-01T07:46:44.350Z
65734004769f3ee9bde1af10
/avatars/d6310ed861972fd691687d8f47413f33.svg
Ying Tai
user
yingtai
TextCrafter: Accurately Rendering Multiple Texts in Complex Visual
  Scenes
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.
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.
Complex Visual Text Generation (CVTG) multi-visual text rendering token focus enhancement CVTG-2K