Product posters blend striking visuals with informative text to highlight the product and capture customer attention. However, crafting appealing posters and manually optimizing them based on online performance is laborious and resource-consuming. To address this, we introduce AutoPP, an automated pipeline for product poster generation and optimization that eliminates the need for human intervention. Specifically, the generator, relying solely on basic product information, first uses a unified design module to integrate the three key elements of a poster (background, text, and layout) into a cohesive output. Then, an element rendering module encodes these elements into condition tokens, efficiently and controllably generating the product poster. Based on the generated poster, the optimizer enhances its Click-Through Rate (CTR) by leveraging online feedback. It systematically replaces elements to gather fine-grained CTR comparisons and utilizes Isolated Direct Preference Optimization (IDPO) to attribute CTR gains to isolated elements. Our work is supported by AutoPP1M, the largest dataset specifically designed for product poster generation and optimization, which contains one million high-quality posters and feedback collected from over one million users. Experiments demonstrate that AutoPP achieves state-of-the-art results in both offline and online settings. Our code and dataset are publicly available at: https://github.com/JD-GenX/AutoPP

翻译：产品海报通过将引人注目的视觉效果与信息丰富的文本相结合，以突出产品特色并吸引顾客关注。然而，手动设计吸引人的海报并根据在线表现进行人工优化，不仅费时费力且消耗大量资源。为解决这一问题，我们提出了AutoPP——一种无需人工干预的自动化产品海报生成与优化流程。具体而言，生成器仅依赖基础产品信息，首先通过统一设计模块将海报的三个关键要素（背景、文本与布局）整合为协调的输出。随后，元素渲染模块将这些要素编码为条件标记，从而高效且可控地生成产品海报。基于生成的海报，优化器利用在线反馈提升其点击率（CTR）。该模块通过系统性地替换元素以收集细粒度的CTR对比数据，并采用孤立直接偏好优化（IDPO）方法将CTR增益归因于独立元素。本研究得到专为产品海报生成与优化构建的最大规模数据集AutoPP1M的支持，该数据集包含一百万张高质量海报及来自超百万用户收集的反馈数据。实验表明，AutoPP在离线与在线场景下均取得了最先进的性能。我们的代码与数据集已公开于：https://github.com/JD-GenX/AutoPP