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    This study introduces ScribbleSense, a novel method for 3D texture editing using multimodal large language models (MLLMs) and image generation. ScribbleSense enhances interactive editing by accurately interpreting user scribbles for 3D asset creation.

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    Area of Science:

    • Computer Graphics
    • Artificial Intelligence
    • Human-Computer Interaction

    Background:

    • Interactive 3D model texture editing is crucial for 3D asset creation.
    • Current methods are limited in utilizing coarse-grained scribble-based interactions.
    • Abstract nature of scribbles leads to ambiguous editing intentions and unclear target locations.

    Purpose of the Study:

    • To develop an effective method for scribble-based 3D texture editing.
    • To address challenges of ambiguous intentions and unclear semantic locations in scribble editing.
    • To leverage multimodal large language models (MLLMs) for improved interactive editing.

    Main Methods:

    • Proposed ScribbleSense method combining MLLMs and image generation models.
    • Utilized MLLMs' visual capabilities to predict editing intent from scribbles.
    • Employed globally generated images to extract local texture details for semantic anchoring.

    Main Results:

    • ScribbleSense effectively interprets semantic intent behind user scribbles.
    • Method alleviates ambiguities in target semantic locations through local detail extraction.
    • Achieved state-of-the-art performance in interactive scribble-based texture editing.

    Conclusions:

    • ScribbleSense successfully integrates MLLMs for enhanced 3D texture editing.
    • The method offers a more intuitive and effective approach to scribble-based interactions.
    • Demonstrates significant advancements in interactive 3D asset creation workflows.