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    This study introduces an automated method for adjusting virtual scene colors to evoke specific moods in virtual reality (VR). The system uses deep learning and a large image dataset to optimize virtual interior design for emotional impact.

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    Area of Science:

    • Computer Graphics
    • Human-Computer Interaction
    • Virtual Reality

    Background:

    • Designing virtual environments to evoke specific moods is challenging due to the subjective nature of emotions.
    • Current virtual reality (VR) scene design lacks automated methods for mood-specific color adjustments.

    Purpose of the Study:

    • To develop a novel approach for automatically adjusting the colors of virtual indoor scene textures to match a target mood.
    • To enhance user immersion and emotional engagement in virtual reality experiences.

    Main Methods:

    • Utilized a dataset of 25,000 interior images to train a deep learning classifier for feature extraction.
    • Developed an optimization process to automatically colorize virtual scenes based on extracted features and target moods.
    • Conducted user studies with VR headsets to evaluate the system's efficacy.

    Main Results:

    • The proposed approach successfully demonstrated the ability to adjust virtual scene colors to align with target moods.
    • Statistical analysis of user study data confirmed the system's effectiveness in influencing the perceived mood of virtual indoor scenes.
    • The method showed promise in creating more emotionally resonant virtual reality environments.

    Conclusions:

    • Automated color adjustment of virtual scene textures is a viable method for mood induction in virtual reality.
    • The developed deep learning-based approach offers a scalable solution for mood-aware virtual environment design.
    • Further research can explore a wider range of moods and scene types.