Abstract
Product posters, which integrate subject, scene, and text, are crucial promotional tools for attracting customers. Creating such posters using modern image generation methods is valuable, while the main challenge lies in accurately rendering text, especially for complex writing systems like Chinese, which contains over 10,000 individual characters. In this work, we identify the key to precise text rendering as constructing a character-discriminative visual feature as a control signal. Based on this insight, we propose a robust character-wise representation as control and we develop TextRenderNet, which achieves a high text rendering accuracy of over 90%. Another challenge in poster generation is maintaining the fidelity of user-specific products. We address this by introducing SceneGenNet, an inpainting-based model, and propose subject fidelity feedback learning to further enhance fidelity. Based on TextRenderNet and SceneGenNet, we present PosterMaker, an end-to-end generation framework. To optimize PosterMaker efficiently, we implement a two-stage training strategy that decouples text rendering and background generation learning. Experimental results show that PosterMaker outperforms existing baselines by a remarkable margin, which demonstrates its effectiveness.
Abstract (translated)
产品海报集成了主题、场景和文本，是吸引顾客的关键促销工具。利用现代图像生成方法创建这样的海报非常有价值，然而主要挑战在于准确渲染文字，尤其是对于包含超过10,000个独特字符的复杂书写系统如中文来说更是如此。在这项工作中，我们确定了精确文字渲染的关键在于构建具有区分度的字符视觉特征作为控制信号。基于这一洞察，我们提出了一种稳健的逐字符表示形式作为控制，并开发了TextRenderNet，实现了超过90%的文字渲染准确率。 海报生成中的另一个挑战是保持用户特定产品的忠实性（保真度）。为了解决这个问题，我们引入了SceneGenNet，这是一个基于图像修复的方法模型，并提出了主题忠实反馈学习以进一步提升保真度。结合TextRenderNet和SceneGenNet，我们推出了PosterMaker，一个端到端的生成框架。 为了高效地优化PosterMaker，我们实施了一种两阶段训练策略，将文字渲染与背景生成的学习过程解耦。实验结果表明，PosterMaker显著优于现有的基准模型，这证明了其有效性。
URL
https://arxiv.org/abs/2504.06632
