AutoPP: Towards Automated Product Poster Generation and Optimization
- URL: http://arxiv.org/abs/2512.21921v1
- Date: Fri, 26 Dec 2025 08:30:32 GMT
- Title: AutoPP: Towards Automated Product Poster Generation and Optimization
- Authors: Jiahao Fan, Yuxin Qin, Wei Feng, Yanyin Chen, Yaoyu Li, Ao Ma, Yixiu Li, Li Zhuang, Haoyi Bian, Zheng Zhang, Jingjing Lv, Junjie Shen, Ching Law,
- Abstract summary: We introduce AutoPP, an automated pipeline for product poster generation and optimization.<n>The generator integrates the three key elements of a poster (background, text, and layout) into a cohesive output.<n>Based on the generated poster, the CTR enhances its Click-Through Rate (CTR) by leveraging online feedback.
- Score: 18.137798180459654
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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
Related papers
- PosterVerse: A Full-Workflow Framework for Commercial-Grade Poster Generation with HTML-Based Scalable Typography [44.93712206658515]
PosterVerse is a full-workflow, commercial-grade poster generation method.<n>PosterVerse replicates professional design through three key stages.<n>PosterDNA is a commercial-grade, HTML-based dataset.
arXiv Detail & Related papers (2026-01-07T15:04:24Z) - Iterative Critique-Refine Framework for Enhancing LLM Personalization [67.77803308645511]
We present PerFine, a unified, training-free critique-refine framework for personalized text generation.<n>In each iteration, an LLM generator produces a draft conditioned on a retrieved profile, and a critic LLM - also conditioned on the same profile - provides structured feedback on tone, vocabulary, sentence structure, and topicality.<n>Across Yelp, Goodreads, and Amazon datasets, PerFine consistently improves personalization over PGraphRAG.
arXiv Detail & Related papers (2025-10-28T14:36:22Z) - Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers [34.9495497884296]
Poster generation is a crucial yet challenging task in scientific communication.<n>We introduce the first benchmark and metric suite for poster generation.<n>PosterAgent is a top-down, visual-in-the-loop multi-agent pipeline.
arXiv Detail & Related papers (2025-05-27T17:58:49Z) - PosterMaker: Towards High-Quality Product Poster Generation with Accurate Text Rendering [50.76106125697899]
Product posters, which integrate subject, scene, and text, are crucial promotional tools for attracting customers.<n>Main challenge lies in accurately rendering text, especially for complex writing systems like Chinese, which contains over 10,000 individual characters.<n>We develop TextRenderNet, which achieves a high text rendering accuracy of over 90%.<n>Based on TextRenderNet and SceneGenNet, we present PosterMaker, an end-to-end generation framework.
arXiv Detail & Related papers (2025-04-09T07:13:08Z) - POSTA: A Go-to Framework for Customized Artistic Poster Generation [87.16343612086959]
POSTA is a modular framework for customized artistic poster generation.<n>Background Diffusion creates a themed background based on user input.<n>Design MLLM then generates layout and typography elements that align with and complement the background style.<n>ArtText Diffusion applies additional stylization to key text elements.
arXiv Detail & Related papers (2025-03-19T05:22:38Z) - CTR-Driven Advertising Image Generation with Multimodal Large Language Models [53.40005544344148]
We explore the use of Multimodal Large Language Models (MLLMs) for generating advertising images by optimizing for Click-Through Rate (CTR) as the primary objective.<n>To further improve the CTR of generated images, we propose a novel reward model to fine-tune pre-trained MLLMs through Reinforcement Learning (RL)<n>Our method achieves state-of-the-art performance in both online and offline metrics.
arXiv Detail & Related papers (2025-02-05T09:06:02Z) - Spatio-Temporal Side Tuning Pre-trained Foundation Models for Video-based Pedestrian Attribute Recognition [58.79807861739438]
Existing pedestrian recognition (PAR) algorithms are mainly developed based on a static image.
We propose to understand human attributes using video frames that can fully use temporal information.
arXiv Detail & Related papers (2024-04-27T14:43:32Z) - Planning and Rendering: Towards Product Poster Generation with Diffusion Models [21.45855580640437]
We propose a novel product poster generation framework based on diffusion models named P&R.
At the planning stage, we propose a PlanNet to generate the layout of the product and other visual components.
At the rendering stage, we propose a RenderNet to generate the background for the product while considering the generated layout.
Our method outperforms the state-of-the-art product poster generation methods on PPG30k.
arXiv Detail & Related papers (2023-12-14T11:11:50Z) - AutoPoster: A Highly Automatic and Content-aware Design System for
Advertising Poster Generation [14.20790443380675]
This paper introduces AutoPoster, a highly automatic and content-aware system for generating advertising posters.
With only product images and titles as inputs, AutoPoster can automatically produce posters of varying sizes through four key stages.
We propose the first poster generation dataset that includes visual attribute annotations for over 76k posters.
arXiv Detail & Related papers (2023-08-02T11:58:43Z) - Personalized Abstractive Summarization by Tri-agent Generation Pipeline [69.38358552893762]
We propose a tri-agent generation pipeline comprising a generator, an instructor, and an editor to enhance output personalization.
The generator produces an initial output, the instructor automatically generates editing instructions based on user preferences, and the editor refines the output to align with those preferences.
We train the instructor using editor-steered reinforcement learning, leveraging feedback from a large-scale editor model to optimize instruction generation.
arXiv Detail & Related papers (2023-05-04T01:12:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.