PresentAgent: Multimodal Agent for Presentation Video Generation

2025-07-05

AI Summary

This work addresses the challenge of automatically generating human-like, audiovisually synchronized presentation videos from long documents. We propose an end-to-end multimodal generation framework comprising a modular pipeline: large language models perform segment-wise document understanding and content distillation; text-to-speech, visual generation, and audiovisual alignment modules jointly produce讲解-style videos with precise image-text matching, semantic coherence, and temporal accuracy. To enable rigorous evaluation, we introduce PresentEval—a unified benchmark that quantitatively assesses content fidelity, visual clarity, and audience comprehension—marking the first such framework for presentation video generation. Experiments on 30 real-world document–presentation pairs demonstrate that our generated videos approach human-produced quality across multiple metrics, significantly advancing the conversion of lengthy textual content into accessible, highly engaging dynamic presentations.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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 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.

Problem

• Transforms long documents into narrated presentation videos
• Generates synchronized visual and spoken content like humans
• Evaluates videos for content fidelity and audience comprehension

Innovation

• Modular pipeline for synchronized audiovisual content
• LLM and TTS for contextual spoken narration
• Vision-Language Model for unified video assessment