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

TL;DR
PresentAgent is a multimodal system that converts long documents into synchronized presentation videos with narration and visuals, evaluated by a new comprehensive assessment framework, achieving near-human quality.
Contribution
We introduce PresentAgent, a novel multimodal pipeline for automatic presentation video generation from documents, including a new evaluation framework, PresentEval.
Findings
Approaches human-level quality in presentation video generation
Effective synchronization of visuals and narration achieved
PresentEval provides comprehensive multimodal video assessment
Abstract
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…
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Code & Models
Videos
