PosterCraft：在统一框架下重新思考高质量美学海报生成

摘要

生成具有美感的海报比简单的设计图像更具挑战性：它不仅需要精确的文本渲染，还要求将抽象的艺术内容、引人注目的布局与整体风格和谐地融为一体。为此，我们提出了PosterCraft，一个统一框架，摒弃了以往模块化的流程和僵化的预设布局，让模型能够自由探索连贯且视觉吸引力强的构图。PosterCraft采用精心设计的级联工作流，以优化高质量美感海报的生成：(i) 在我们新引入的Text-Render-2M数据集上进行大规模文本渲染优化；(ii) 在HQ-Poster100K上进行区域感知的监督微调；(iii) 通过最佳n项偏好优化实现美感文本强化学习；(iv) 结合视觉-语言反馈进行联合精炼。每个阶段都配备了一个完全自动化的数据构建流程，针对其特定需求定制，无需复杂的架构修改即可实现稳健训练。通过多项实验评估，PosterCraft在渲染准确性、布局连贯性和整体视觉吸引力方面显著超越了开源基线，接近了SOTA商业系统的水平。我们的代码、模型和数据集可在项目页面找到：https://ephemeral182.github.io/PosterCraft。

English

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：在统一框架下重新思考高质量美学海报生成

PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework

摘要

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