Generating photo manipulation tutorials by demonstration

Abstract

We present a demonstration-based system for automatically generating succinct step-by-step visual tutorials of photo manipulations. An author first demonstrates the manipulation using an instrumented version of GIMP that records all changes in interface and application state. From the example recording, our system automatically generates tutorials that illustrate the manipulation using images, text, and annotations. It leverages automated image labeling (recognition of facial features and outdoor scene structures in our implementation) to generate more precise text descriptions of many of the steps in the tutorials. A user study comparing our automatically generated tutorials to hand-designed tutorials and screen-capture video recordings finds that users are 20--44% faster and make 60--95% fewer errors using our tutorials. While our system focuses on tutorial generation, we also present some initial work on generating content-dependent macros that use image recognition to automatically transfer selection operations from the example image used in the demonstration to new target images. While our macros are limited to transferring selection operations we demonstrate automatic transfer of several common retouching techniques including eye recoloring, whitening teeth and sunset enhancement.

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Index Terms

• Computing methodologies

  • Artificial intelligence

    • Computer vision

      • Computer vision problems

      • Computer vision tasks

        • Scene understanding

      • Image and video acquisition

        • 3D imaging

  • Computer graphics

    • Animation

    • Shape modeling

  • Machine learning

• Theory of computation

  • Randomness, geometry and discrete structures

    • Computational geometry