PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a
  Unified Framework
Authors: name: SiXiang Chen, name: Jianyu Lai, name: Jialin Gao, name: Tian Ye, name: Haoyu Chen, name: Hengyu Shi, name: Shitong Shao, name: Yunlong Lin, name: Song Fei, name: Zhaohu Xing, name: Yeying Jin, name: Junfeng Luo, name: Xiaoming Wei, name: Lei Zhu

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