Generating Narrated Lecture Videos from Slides with Synchronized Highlights

TL;DR

This paper presents an automated system that converts slide decks into engaging narrated video lectures with synchronized visual highlights, leveraging advanced alignment techniques for precise attention guidance.

Contribution

The novel highlight alignment module accurately maps spoken phrases to slide locations using multiple strategies, enabling scalable and cost-effective video lecture generation.

Findings

High location accuracy with F1 > 92% using LLM-based alignment

Significant cost reduction to under $1 per hour of video

Effective handling of complex, math-heavy slide content

Abstract

Turning static slides into engaging video lectures takes considerable time and effort, requiring presenters to record explanations and visually guide their audience through the material. We introduce an end-to-end system designed to automate this process entirely. Given a slide deck, this system synthesizes a video lecture featuring AI-generated narration synchronized precisely with dynamic visual highlights. These highlights automatically draw attention to the specific concept being discussed, much like an effective presenter would. The core technical contribution is a novel highlight alignment module. This module accurately maps spoken phrases to locations on a given slide using diverse strategies (e.g., Levenshtein distance, LLM-based semantic analysis) at selectable granularities (line or word level) and utilizes timestamp-providing Text-to-Speech (TTS) for timing synchronization. We demonstrate the system's effectiveness through a technical evaluation using a manually annotated slide dataset with 1000 samples, finding that LLM-based alignment achieves high location accuracy (F1 > 92%), significantly outperforming simpler methods, especially on complex, math-heavy content. Furthermore, the calculated generation cost averages under $1 per hour of video, offering potential savings of two orders of magnitude compared to conservative estimates of manual production costs. This combination of high accuracy and extremely low cost positions this approach as a practical and scalable tool for transforming static slides into effective, visually-guided video lectures.