P re G enie: An Agentic Framework for High-quality Visual Presentation Generation (2025)

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.

## introduction visual presentations play an important role in visual communication. they are frequently used in speeches, reports, and various forms of concept expression, helping to enhance audience understanding. early visual presentations relied on manual creation, which was time-consuming and repetitive. in recent years, with the rapid development of generative models and mllms @xcite @xcite , many efforts have been dedicated to automating the generation of visual presentations. however, the task of automatically generating visual presentations faces sev- figure 1 : the pregenie framework, powered by mllms, processes text-image inputs to generate highquality visual presentations. source: @xcite eral challenges: (1) aesthetic and well-organized layouts: from a design perspective, the generated slide content must be well-arranged and meet human aesthetic standards. (2) support for complex multimodal inputs and outputs: the model needs to understand complex text-image inputs (e.g., a research paper or blog) and generate slides containing both text and images. (3) accuracy and relevance between text and images: the generated text must closely align with the main ideas of the original content, and the images on each slide must be highly relevant to the accompanying text to aid audience comprehension. existing work on visual presentation generation can be divided into two categories: (1) direct generation of text-image slides @xcite . these methods, often using generative models such as diffusion models, embed text directly into images. while the layouts generated by these methods are often visually appealing, they cannot handle complex text-image inputs. additionally, the generated results are not editable, and content accuracy cannot be guaranteed. as a result, these methods are unsuitable for fields with high requirements for professionalism and precision, such as scientific research. (2) generation of intermediate code followed by rendering @xcite @xcite . currently, this is the more widely adopted approach. these methods first generate intermediate code, which is then rendered into slides using predefined templates or rules. however, there is often a gap between the intermediate code and the final visual results, making it difficult to ensure the slides are harmonious and visually appealing. moreover, while these methods generally handle text well, they struggle to understand images in the input document, and most of them only generate plaintext slides without images @xcite . this raises the question: can we develop a framework that supports multimodal inputs and generates visual presentations that are aesthetically pleasing with accurate and consistent content? in this work, we propose pregenie, an agentic framework based on mllms that supports textimage document inputs and generates visually appealing, accurate, and coherent visual presentations. it leverages slidev 1 , a markdown-based, flexible, and concise presentation framework. compared to prior works that use intermediate code representations @xcite (e.g., html or python's pptx library), slidev provides a simpler structure, reducing the difficulty for llms to generate correct code and making subsequent editing easier. based on the slidev framework, our workflow consists of two stages: (1) analysis and initial generation: this stage involves syntax analysis, text summarization, and image tagging and positioning to generate visual presentations that closely align with the input document. (2) review and re-generation: this includes code review and page review. code review uses large language models(llms) to check for formatting and content errors in the intermediate code. building on this, page review uses vision-language models (vlms) to examine the final visual results page by page, identifying errors that may be missed in the code (e.g., an image partially overflowing the slide). after these checks, problematic slides are regenerated. while previous works on code generation have 1 https://sli.dev/ widely adopted code review mechanisms @xcite , the lack of a visual inspection mechanism has hindered the quality of the final slide outputs. by introducing a page review mechanism, we significantly improve the alignment and aesthetics of the layouts, effectively closing the gap between intermediate code and the final visual results. extensive experiments and evaluations demonstrate that our method excels in design aesthetics, text coverage, and text-image consistency. overall, our contributions are as follows: ## method inspired by human workflows, we decompose the task of presentation generation into multi-stages and a series of fine-grained steps. our approach integrates five llms and vlms that share a common context. as shown in fig. 2 , the framework starts with an input consisting of a webpage or a text-image document and processes it sequentially through the text summarizer, image captioner, code generator, code reviewer, and page reviewer, ultimately producing high-quality textimage presentations. among these components, the text summarizer, code generator, and code reviewer are powered by llms, while the image captioner and page reviewer utilize vlms. the entire process is divided into two stages. the first stage, analysis and initial generation (fig. 2 top), focuses on performing a fundamental analysis of the input multimodal information and generating the initial version of the code for subsequent processing. the second stage, iterative review and re-generation (fig. 2 bottom), takes the output from the first stage and refines it through iterative checks of the code and visual elements of the rendered pages. this process ultimately results in refined and visually appealing slides. ## experiment we present the implementation details of our framework, along with its qualitative and quantitative results, as well as comparisons with other state-ofthe-art models, including pptagent (zheng et al., 2025), kctv @xcite , and autopresent @xcite . ## applications with minimal effort in modifying llm prompts, our framework can support a wide range of slide generation applications, extending beyond textimage documents. for instance, when generating slides from text-only input, fig. 8 illustrates an example of storyboard creation. additionally, our framework can extract content from poorly designed existing slides and reformat them with enhanced layouts, as demonstrated in fig. 9 . ## conclusion in this paper, we present pregenie, an agentic framework powered by mllms for generating high-quality visual presentations. built on the slidev framework, pregenie summarizes multimodal input, generates markdown code, and refines the code and slides iteratively. experiments show that pregenie outperforms existing models in aesthetics, content consistency, and alignment with human design preferences. pregenie also supports diverse practical applications, offering an effective solution for visual presentation generation.