TextAtlas5M: A Large-scale Dataset for Dense Text Image Generation
Text-conditioned image generation has gained significant attention in recent
years and are processing increasingly longer and comprehensive text prompt. In
everyday life, dense and intricate text appears in contexts like
advertisements, infographics, and signage, where the integration of both text
and visuals is essential for conveying complex information. However, despite
these advances, the generation of images containing long-form text remains a
persistent challenge, largely due to the limitations of existing datasets,
which often focus on shorter and simpler text. To address this gap, we
introduce TextAtlas5M, a novel dataset specifically designed to evaluate
long-text rendering in text-conditioned image generation. Our dataset consists
of 5 million long-text generated and collected images across diverse data
types, enabling comprehensive evaluation of large-scale generative models on
long-text image generation. We further curate 3000 human-improved test set
TextAtlasEval across 3 data domains, establishing one of the most extensive
benchmarks for text-conditioned generation. Evaluations suggest that the
TextAtlasEval benchmarks present significant challenges even for the most
advanced proprietary models (e.g. GPT4o with DallE-3), while their open-source
counterparts show an even larger performance gap. These evidences position
TextAtlas5M as a valuable dataset for training and evaluating future-generation
text-conditioned image generation models.
67ad79cb60ec3f444b21cbcb
Alex Jinpeng Wang
67ad79cb60ec3f444b21cbcc
Dongxing Mao
67ad79cb60ec3f444b21cbcd
Jiawei Zhang
67ad79cb60ec3f444b21cbce
Weiming Han
67ad79cb60ec3f444b21cbcf
Zhuobai Dong
67ad79cb60ec3f444b21cbd0
Linjie Li
67ad79cb60ec3f444b21cbd1
Yiqi Lin
67ad79cb60ec3f444b21cbd2
Zhengyuan Yang
67ad79cb60ec3f444b21cbd3
Libo Qin
67ad79cb60ec3f444b21cbd4
Fuwei Zhang
claimed_verified
2025-02-13T20:35:54.519Z
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/avatars/4e29a953db4edee0abfc3485be4faf3d.svg
zhangfuwei
user
fuwei
67ad79cb60ec3f444b21cbd5
Lijuan Wang
67ad79cb60ec3f444b21cbd6
Min Li
TextAtlas5M: A Large-scale Dataset for Dense Text Image Generation
Text-conditioned image generation has gained significant attention in recent
years and are processing increasingly longer and comprehensive text prompt. In
everyday life, dense and intricate text appears in contexts like
advertisements, infographics, and signage, where the integration of both text
and visuals is essential for conveying complex information. However, despite
these advances, the generation of images containing long-form text remains a
persistent challenge, largely due to the limitations of existing datasets,
which often focus on shorter and simpler text. To address this gap, we
introduce TextAtlas5M, a novel dataset specifically designed to evaluate
long-text rendering in text-conditioned image generation. Our dataset consists
of 5 million long-text generated and collected images across diverse data
types, enabling comprehensive evaluation of large-scale generative models on
long-text image generation. We further curate 3000 human-improved test set
TextAtlasEval across 3 data domains, establishing one of the most extensive
benchmarks for text-conditioned generation. Evaluations suggest that the
TextAtlasEval benchmarks present significant challenges even for the most
advanced proprietary models (e.g. GPT4o with DallE-3), while their open-source
counterparts show an even larger performance gap. These evidences position
TextAtlas5M as a valuable dataset for training and evaluating future-generation
text-conditioned image generation models.
TextAtlas5M is a novel dataset of 5 million images for evaluating long-text rendering in text-conditioned image generation, presenting challenges even for advanced proprietary models.
text-conditioned image generation TextAtlas5M TextAtlasEval long-form text GPT4o DallE-3 generative models