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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Perceptually Based Adaptive Motion Retargeting to Animate Real Objects by Light Projection.

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    This study introduces an optimized light projection method to create realistic motion illusions on objects. The new framework minimizes visual inconsistencies, enhancing the projection effect for dynamic visual experiences.

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    Area of Science:

    • Computer Vision
    • Human-Computer Interaction
    • Visual Perception

    Background:

    • A novel light projection technique creates illusory motion on static objects without altering surface properties.
    • This method leverages gray-scale motion-inducers to activate human visual motion detectors.
    • The technique is effective in bright conditions due to weaker pattern shifts, but manual adjustment of deformation is time-consuming.

    Purpose of the Study:

    • To develop an automated optimization framework for adaptive displacement vector retargeting.
    • To enhance the illusory motion effect while maintaining perceptual consistency with the original object surface.
    • To reduce manual effort in achieving satisfactory projection results.

    Main Methods:

    • An optimization framework was proposed to adaptively retarget displacement vectors.
    • A perceptual model was developed to predict subjective inconsistency by simulating human visual system responses.
    • Displacement vectors were optimized to maximize projection effect within the model's predicted tolerance range.

    Main Results:

    • The optimization framework successfully adapted displacement vectors based on the perceptual model.
    • The proposed method aims to maximize the illusory motion effect within acceptable perceptual limits.
    • User studies and psychophysical experiments validated the effectiveness of the perceptual model and optimization approach.

    Conclusions:

    • The developed optimization framework overcomes limitations of manual adjustment in light projection techniques.
    • The perceptual model accurately predicts visual inconsistencies, enabling adaptive retargeting of displacement vectors.
    • This approach enhances the creation of dynamic visual impressions on static objects, improving user experience and efficiency.