Beyond Play and Pause: Turning GPT-4o Spatial Weakness into a Strength for In-Depth Interactive Video Learning

TL;DR

This paper presents Untwist, an AI system that transforms passive video learning into an interactive experience by enabling region-specific questions and responses, leveraging GPT-4o's spatial weaknesses with annotated frames for improved accuracy.

Contribution

The paper introduces Untwist, a novel system integrating GPT APIs and computer vision to enable real-time, region-specific video interaction, addressing GPT-4o's spatial limitations with annotated frames.

Findings

Enhanced accuracy in localizing video content using annotated frames

Successful integration of GPT APIs with computer vision for interactive responses

Potential to significantly increase engagement and comprehension in video learning

Abstract

Traditional video-based learning remains passive, offering limited opportunities for users to engage dynamically with content. While current AI-powered tools offer transcription and summarization, they lack real-time, region-specific interaction capabilities. This paper introduces Untwist, an AI-driven system that enables interactive video learning by allowing users to ask questions about the entire video or specific regions using a bounding box, receiving context-aware, multimodal responses. By integrating GPT APIs with Computer Vision techniques, Untwist extracts, processes, and structures video content to enhance comprehension. Our approach addresses GPT-4o spatial weakness by leveraging annotated frames instead of raw coordinate data, significantly improving accuracy in localizing and interpreting video content. This paper describes the system architecture, including video pre-processing and real-time interaction, and outlines how Untwist can transform passive video consumption into an interactive, AI-driven learning experience with the potential to enhance engagement and comprehension.