AutoPP: Towards Automated Product Poster Generation and Optimization
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908732737191936 |
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| author | Fan, Jiahao Qin, Yuxin Feng, Wei Chen, Yanyin Li, Yaoyu Ma, Ao Li, Yixiu Zhuang, Li Bian, Haoyi Zhang, Zheng Lv, Jingjing Shen, Junjie Law, Ching |
| author_facet | Fan, Jiahao Qin, Yuxin Feng, Wei Chen, Yanyin Li, Yaoyu Ma, Ao Li, Yixiu Zhuang, Li Bian, Haoyi Zhang, Zheng Lv, Jingjing Shen, Junjie Law, Ching |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_21921 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AutoPP: Towards Automated Product Poster Generation and Optimization Fan, Jiahao Qin, Yuxin Feng, Wei Chen, Yanyin Li, Yaoyu Ma, Ao Li, Yixiu Zhuang, Li Bian, Haoyi Zhang, Zheng Lv, Jingjing Shen, Junjie Law, Ching Computer Vision and Pattern Recognition Information Retrieval Machine Learning 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 |
| title | AutoPP: Towards Automated Product Poster Generation and Optimization |
| topic | Computer Vision and Pattern Recognition Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2512.21921 |