PresentAgent: Multimodal Agent for Presentation Video Generation
Authors: name: Jingwei Shi, name: Zeyu Zhang, name: Biao Wu, name: Yanjie Liang, name: Meng Fang, name: Ling Chen, name: Yang Zhao

We present PresentAgent, a multimodal agent that transforms long-form
documents into narrated presentation videos. While existing approaches are
limited to generating static slides or text summaries, our method advances
beyond these limitations by producing fully synchronized visual and spoken
content that closely mimics human-style presentations. To achieve this
integration, PresentAgent employs a modular pipeline that systematically
segments the input document, plans and renders slide-style visual frames,
generates contextual spoken narration with large language models and
Text-to-Speech models, and seamlessly composes the final video with precise
audio-visual alignment. Given the complexity of evaluating such multimodal
outputs, we introduce PresentEval, a unified assessment framework powered by
Vision-Language Models that comprehensively scores videos across three critical
dimensions: content fidelity, visual clarity, and audience comprehension
through prompt-based evaluation. Our experimental validation on a curated
dataset of 30 document-presentation pairs demonstrates that PresentAgent
approaches human-level quality across all evaluation metrics. These results
highlight the significant potential of controllable multimodal agents in
transforming static textual materials into dynamic, effective, and accessible
presentation formats. Code will be available at
https://github.com/AIGeeksGroup/PresentAgent.