PreGenie: An Agentic Framework for High-quality Visual Presentation Generation

• URL: http://arxiv.org/abs/2505.21660v1
• Date: Tue, 27 May 2025 18:36:19 GMT
• Title: PreGenie: An Agentic Framework for High-quality Visual Presentation Generation
• Authors: Xiaojie Xu, Xinli Xu, Sirui Chen, Haoyu Chen, Fan Zhang, Ying-Cong Chen,
• Abstract summary: PreGenie is an agentic and modular framework powered by multimodal large language models (MLLMs) for generating high-quality visual presentations.<n>It operates in two stages: (1) Analysis and Initial Generation, which summarizes multimodal input and generates initial code, and (2) Review and Re-generation, which iteratively reviews intermediate code and rendered slides to produce final, high-quality presentations.
• Score: 25.673526096069548
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual presentations are vital for effective communication. Early attempts to automate their creation using deep learning often faced issues such as poorly organized layouts, inaccurate text summarization, and a lack of image understanding, leading to mismatched visuals and text. These limitations restrict their application in formal contexts like business and scientific research. To address these challenges, we propose PreGenie, an agentic and modular framework powered by multimodal large language models (MLLMs) for generating high-quality visual presentations. PreGenie is built on the Slidev presentation framework, where slides are rendered from Markdown code. It operates in two stages: (1) Analysis and Initial Generation, which summarizes multimodal input and generates initial code, and (2) Review and Re-generation, which iteratively reviews intermediate code and rendered slides to produce final, high-quality presentations. Each stage leverages multiple MLLMs that collaborate and share information. Comprehensive experiments demonstrate that PreGenie excels in multimodal understanding, outperforming existing models in both aesthetics and content consistency, while aligning more closely with human design preferences.

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