TutoAI introduces a framework for AI-assisted mixed-media tutorial creation. It identifies common components, assembles and evaluates AI models, and proposes UI guidelines. The framework aims to improve the quality of tutorials compared to baseline methods through comprehensive surveys and empirical evaluations.
Instructional videos are essential sources for learning new skills. Mixed-media tutorials offer more interactive alternatives than traditional videos but are challenging to create manually. TutoAI addresses this by leveraging AI models to extract components and design user-friendly interfaces.
The framework focuses on physical tasks like cooking and crafting, aiming to generalize the creation process across different domains. By combining text summarization, NLVL methods, shot boundary detection, and open-vocabulary object detectors, TutoAI enhances the efficiency of tutorial creation.
Through manual comparisons and quantitative evaluations, TutoAI demonstrates promising results in step extraction accuracy and object identification across diverse instructional video domains. The UI design considerations prioritize component-based creation, modality separation, editable outputs, and real-time edit previews.
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