title: BizGen: Advancing Article-level Visual Text Rendering for Infographics
    Generation
summary: Recently, state-of-the-art text-to-image generation models, such as Flux and
  Ideogram 2.0, have made significant progress in sentence-level visual text
  rendering. In this paper, we focus on the more challenging scenarios of
  article-level visual text rendering and address a novel task of generating
  high-quality business content, including infographics and slides, based on user
  provided article-level descriptive prompts and ultra-dense layouts. The
  fundamental challenges are twofold: significantly longer context lengths and
  the scarcity of high-quality business content data.
    In contrast to most previous works that focus on a limited number of
  sub-regions and sentence-level prompts, ensuring precise adherence to
  ultra-dense layouts with tens or even hundreds of sub-regions in business
  content is far more challenging. We make two key technical contributions: (i)
  the construction of scalable, high-quality business content dataset, i.e.,
  Infographics-650K, equipped with ultra-dense layouts and prompts by
  implementing a layer-wise retrieval-augmented infographic generation scheme;
  and (ii) a layout-guided cross attention scheme, which injects tens of
  region-wise prompts into a set of cropped region latent space according to the
  ultra-dense layouts, and refine each sub-regions flexibly during inference
  using a layout conditional CFG.
    We demonstrate the strong results of our system compared to previous SOTA
  systems such as Flux and SD3 on our BizEval prompt set. Additionally, we
  conduct thorough ablation experiments to verify the effectiveness of each
  component. We hope our constructed Infographics-650K and BizEval can encourage
  the broader community to advance the progress of business content generation.
paper_authors._id: 67e4b82c672b3d9c9cb42c70
paper_authors.name: Yuyang Peng
paper_authors._id: 67e4b82c672b3d9c9cb42c71
paper_authors.name: Shishi Xiao
paper_authors._id: 67e4b82c672b3d9c9cb42c72
paper_authors.name: Keming Wu
paper_authors.status: admin_assigned
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paper_authors.fullname: Keming Wu
paper_authors.type: user
paper_authors.user: wukeming11
paper_authors._id: 67e4b82c672b3d9c9cb42c73
paper_authors.name: Qisheng Liao
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paper_authors.avatarUrl: /avatars/78297eadad816c45d680aa70cea3b973.svg
paper_authors.fullname: QISHENG LIAO
paper_authors.type: user
paper_authors.user: Marseclipse
paper_authors._id: 67e4b82c672b3d9c9cb42c74
paper_authors.name: Bohan Chen
paper_authors.status: admin_assigned
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paper_authors.fullname: chen
paper_authors.type: user
paper_authors.user: bohanChen
paper_authors._id: 67e4b82c672b3d9c9cb42c75
paper_authors.name: Kevin Lin
paper_authors.status: admin_assigned
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paper_authors.fullname: Kevin Lin
paper_authors.type: user
paper_authors.user: kevinlin311tw
paper_authors._id: 67e4b82c672b3d9c9cb42c76
paper_authors.name: Danqing Huang
paper_authors._id: 67e4b82c672b3d9c9cb42c77
paper_authors.name: Ji Li
paper_authors._id: 67e4b82c672b3d9c9cb42c78
paper_authors.name: Yuhui Yuan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-27T09:56:39.210Z
paper_authors._id: 631f108bb45367a05fe74260
paper_authors.avatarUrl: /avatars/c20da6800b643728062712d9a2771648.svg
paper_authors.fullname: Researcher
paper_authors.type: user
paper_authors.user: YuanYuhui
paper_title: BizGen: Advancing Article-level Visual Text Rendering for Infographics
    Generation
paper_summary: Recently, state-of-the-art text-to-image generation models, such as Flux and
  Ideogram 2.0, have made significant progress in sentence-level visual text
  rendering. In this paper, we focus on the more challenging scenarios of
  article-level visual text rendering and address a novel task of generating
  high-quality business content, including infographics and slides, based on user
  provided article-level descriptive prompts and ultra-dense layouts. The
  fundamental challenges are twofold: significantly longer context lengths and
  the scarcity of high-quality business content data.
    In contrast to most previous works that focus on a limited number of
  sub-regions and sentence-level prompts, ensuring precise adherence to
  ultra-dense layouts with tens or even hundreds of sub-regions in business
  content is far more challenging. We make two key technical contributions: (i)
  the construction of scalable, high-quality business content dataset, i.e.,
  Infographics-650K, equipped with ultra-dense layouts and prompts by
  implementing a layer-wise retrieval-augmented infographic generation scheme;
  and (ii) a layout-guided cross attention scheme, which injects tens of
  region-wise prompts into a set of cropped region latent space according to the
  ultra-dense layouts, and refine each sub-regions flexibly during inference
  using a layout conditional CFG.
    We demonstrate the strong results of our system compared to previous SOTA
  systems such as Flux and SD3 on our BizEval prompt set. Additionally, we
  conduct thorough ablation experiments to verify the effectiveness of each
  component. We hope our constructed Infographics-650K and BizEval can encourage
  the broader community to advance the progress of business content generation.
paper_ai_summary: A scalable dataset and layout-guided cross-attention scheme enhance text-to-image generation for article-level business content with ultra-dense layouts.
paper_ai_keywords: text-to-image generation Flux Ideogram 2.0 article-level visual text rendering infographics slides ultra-dense layouts Infographics-650K layer-wise retrieval-augmented infographic generation layout-guided cross attention CFG BizEval