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:this https URL

@article{chen2025_2506.10741,
  title={ PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework },
  author={ SiXiang Chen and Jianyu Lai and Jialin Gao and Tian Ye and Haoyu Chen and Hengyu Shi and Shitong Shao and Yunlong Lin and Song Fei and Zhaohu Xing and Yeying Jin and Junfeng Luo and Xiaoming Wei and Lei Zhu },
  journal={arXiv preprint arXiv:2506.10741},
  year={ 2025 }
}