PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework
Abstract
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 finetuning on HQ-Poster-100K; (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.
Cite
Text
Chen et al. "PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework." International Conference on Learning Representations, 2026.Markdown
[Chen et al. "PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework." International Conference on Learning Representations, 2026.](https://mlanthology.org/iclr/2026/chen2026iclr-postercraft/)BibTeX
@inproceedings{chen2026iclr-postercraft,
title = {{PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework}},
author = {Chen, Sixiang and Lai, Jianyu and Gao, Jialin and Ye, Tian and Chen, Haoyu and Shi, Hengyu and Shao, Shitong and Lin, Yunlong and Fei, Song and Xing, Zhaohu and Jin, Yeying and Luo, Junfeng and Wei, Xiaoming and Zhu, Lei},
booktitle = {International Conference on Learning Representations},
year = {2026},
url = {https://mlanthology.org/iclr/2026/chen2026iclr-postercraft/}
}