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PreGenie: An Agentic Framework for High-quality Visual Presentation Generation
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PreGenie: An Agentic Framework for High-quality Visual Presentation Generation
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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.
Forward citations
Cited by 6 Pith papers
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DeepSlide: From Artifacts to Presentation Delivery
DeepSlide introduces a multi-agent system for full presentation preparation that matches baselines on slide quality but improves narrative flow, pacing, and script synergy via a new dual-scoreboard benchmark.
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DECKBench: Benchmarking Multi-Agent Frameworks for Academic Slide Generation and Editing
A benchmark for paper-to-slide generation and multi-turn editing, built from 294 pairs and simulated users, with an editing-evaluation design that is partly circular.
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