presentation-video-generation

name	presentation-video-generation
title	PresentAgent: Multimodal Agent for Presentation Video Generation
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2507.04036
keywords	["Presentation Generation","Multimodal AI","Video Synthesis","Document Processing","Audio-Visual Alignment"]
description	Transform lengthy documents into fully narrated presentation videos with synchronized audio-visual delivery. Automatically segments content, generates visuals, synthesizes speech, and composes final video.

PresentAgent: Automatic Presentation Video Generation from Documents

Creating presentation videos from documents requires coordinating multiple modalities: extracting key ideas, designing visuals, writing scripts, synthesizing speech, and synchronizing everything. Current approaches either generate static slides or disconnected video clips. PresentAgent bridges this gap by orchestrating the entire workflow—from document parsing to final video composition—maintaining semantic coherence and temporal alignment throughout.

The core insight is that presentation generation is a modular pipeline problem. Each stage (segmentation, visual design, narration, assembly) has distinct requirements and can be optimized independently, yet must coordinate carefully through shared semantic understanding.

Core Concept

PresentAgent operates as a four-stage sequential pipeline where each stage builds on the previous one while maintaining focus on the original document's intent:

1. Document segmentation breaks lengthy inputs into coherent content blocks
2. Slide generation creates visually-aligned layouts for each block
3. Narration synthesis converts key messages into natural spoken audio
4. Video assembly synchronizes visuals and audio into a final presentation

This modular design allows each component to be replaced or upgraded independently while maintaining end-to-end coherence.

Architecture Overview

• Document parser: Extracts semantic structure through outline planning
• Slide renderer: Generates layout-guided visual frames with text and imagery
• Script generator: LLM-based conversion of content to oral-style narration scripts
• Text-to-speech engine: Synthesizes high-quality audio from scripts
• Video compositor: Aligns slides temporally with audio duration
• PresentEval framework: Vision-language model evaluation of content fidelity, visual clarity, and comprehension

Implementation

Start by parsing your document and extracting the outline structure:

import json
from presentagent.parser import DocumentParser
from presentagent.planner import ContentPlanner

# Load and segment the document
parser = DocumentParser()
document_text = open().read()


outline = parser.create_outline(document_text)
content_blocks = parser.segment_by_outline(document_text, outline)


 i, block  (content_blocks):
    ()
    ()