BizGen: Advancing Article-level Visual Text Rendering for Infographics
  Generation
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.
67e4b82c672b3d9c9cb42c70
Yuyang Peng
67e4b82c672b3d9c9cb42c71
Shishi Xiao
67e4b82c672b3d9c9cb42c72
Keming Wu
admin_assigned
2025-03-27T09:57:20.576Z
66bf00ca5b4e241fe266059d
https://cdn-avatars.huggingface.co/v1/production/uploads/66bf00ca5b4e241fe266059d/PPIy6XxFSqgOjQyECdj4p.jpeg
Keming Wu
user
wukeming11
67e4b82c672b3d9c9cb42c73
Qisheng Liao
admin_assigned
2025-03-27T09:57:14.624Z
672894ff1905afcdc9132fc6
/avatars/78297eadad816c45d680aa70cea3b973.svg
QISHENG LIAO
user
Marseclipse
67e4b82c672b3d9c9cb42c74
Bohan Chen
admin_assigned
2025-03-27T09:57:07.459Z
64ba249e5c4deebf69aa17fd
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/h7YdQe9wEr1TTJfP1ALhb.jpeg
chen
user
bohanChen
67e4b82c672b3d9c9cb42c75
Kevin Lin
admin_assigned
2025-03-27T09:57:01.691Z
6298fd95b58e71e2ac9f3ad8
/avatars/7d34644d537bc5c17cf1e4ce4095355c.svg
Kevin Lin
user
kevinlin311tw
67e4b82c672b3d9c9cb42c76
Danqing Huang
67e4b82c672b3d9c9cb42c77
Ji Li
67e4b82c672b3d9c9cb42c78
Yuhui Yuan
admin_assigned
2025-03-27T09:56:39.210Z
631f108bb45367a05fe74260
/avatars/c20da6800b643728062712d9a2771648.svg
Researcher
user
YuanYuhui
BizGen: Advancing Article-level Visual Text Rendering for Infographics
  Generation
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.
A scalable dataset and layout-guided cross-attention scheme enhance text-to-image generation for article-level business content with ultra-dense layouts.
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