PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a
  Unified Framework
Generating aesthetic posters is more challenging than simple design images:
it requires not only precise text rendering but also the seamless integration
of abstract artistic content, striking layouts, and overall stylistic harmony.
To address this, we propose PosterCraft, a unified framework that abandons
prior modular pipelines and rigid, predefined layouts, allowing the model to
freely explore coherent, visually compelling compositions. PosterCraft employs
a carefully designed, cascaded workflow to optimize the generation of
high-aesthetic posters: (i) large-scale text-rendering optimization on our
newly introduced Text-Render-2M dataset; (ii) region-aware supervised
fine-tuning on HQ-Poster100K; (iii) aesthetic-text-reinforcement learning via
best-of-n preference optimization; and (iv) joint vision-language feedback
refinement. Each stage is supported by a fully automated data-construction
pipeline tailored to its specific needs, enabling robust training without
complex architectural modifications. Evaluated on multiple experiments,
PosterCraft significantly outperforms open-source baselines in rendering
accuracy, layout coherence, and overall visual appeal-approaching the quality
of SOTA commercial systems. Our code, models, and datasets can be found in the
Project page: https://ephemeral182.github.io/PosterCraft
684b881f3b733ba333686fd4
SiXiang Chen
claimed_verified
2025-06-13T07:39:47.414Z
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sixiang chen
user
Ephemeral182
684b881f3b733ba333686fd5
Jianyu Lai
684b881f3b733ba333686fd6
Jialin Gao
684b881f3b733ba333686fd7
Tian Ye
claimed_verified
2025-06-15T07:03:56.052Z
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Owen
user
Owen777
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Haoyu Chen
684b881f3b733ba333686fd9
Hengyu Shi
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Shitong Shao
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Yunlong Lin
claimed_verified
2025-06-16T07:16:55.384Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/64ecb174f22081b4ac7ca397/PiAPtD_rbuhGOqfE6ZSIu.jpeg
Yunlong Lin
user
LYL1015
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Song Fei
684b881f3b733ba333686fdd
Zhaohu Xing
684b881f3b733ba333686fde
Yeying Jin
claimed_verified
2025-06-23T08:31:21.380Z
68563e56c09959da82e70968
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/EK3q7Paw0ysO_-WYU2Eu3.jpeg
Yeying Jin
user
yeyingjin
684b881f3b733ba333686fdf
Junfeng Luo
684b881f3b733ba333686fe0
Xiaoming Wei
684b881f3b733ba333686fe1
Lei Zhu
PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a
  Unified Framework
Generating aesthetic posters is more challenging than simple design images:
it requires not only precise text rendering but also the seamless integration
of abstract artistic content, striking layouts, and overall stylistic harmony.
To address this, we propose PosterCraft, a unified framework that abandons
prior modular pipelines and rigid, predefined layouts, allowing the model to
freely explore coherent, visually compelling compositions. PosterCraft employs
a carefully designed, cascaded workflow to optimize the generation of
high-aesthetic posters: (i) large-scale text-rendering optimization on our
newly introduced Text-Render-2M dataset; (ii) region-aware supervised
fine-tuning on HQ-Poster100K; (iii) aesthetic-text-reinforcement learning via
best-of-n preference optimization; and (iv) joint vision-language feedback
refinement. Each stage is supported by a fully automated data-construction
pipeline tailored to its specific needs, enabling robust training without
complex architectural modifications. Evaluated on multiple experiments,
PosterCraft significantly outperforms open-source baselines in rendering
accuracy, layout coherence, and overall visual appeal-approaching the quality
of SOTA commercial systems. Our code, models, and datasets can be found in the
Project page: https://ephemeral182.github.io/PosterCraft
PosterCraft improves aesthetic poster generation through a unified, modular pipeline with enhanced text rendering, region-aware fine-tuning, aesthetic reinforcement learning, and joint vision-language refinement.
text-rendering optimization Text-Render-2M region-aware supervised fine-tuning HQ-Poster100K aesthetic-text-reinforcement learning best-of-n preference optimization joint vision-language feedback refinement