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Real-Time 3D Eye Performance Reconstruction for RGBD Cameras.

Quan Wen, Feng Xu, Jun-Hai Yong

    IEEE Transactions on Visualization and Computer Graphics
    |December 28, 2016
    PubMed
    Summary

    This study introduces a real-time 3D eye performance reconstruction method using an RGBD sensor. The technique accurately captures vivid eye motions and handles various conditions for enhanced facial performance.

    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Human-Computer Interaction

    Background:

    • Accurate 3D eye performance reconstruction is crucial for realistic facial animation.
    • Existing methods often struggle with real-time performance, diverse appearances, and occlusions.

    Purpose of the Study:

    • To propose a novel real-time method for 3D eye performance reconstruction using a single RGBD sensor.
    • To enhance facial performance with vivid and accurate eye motion capture.

    Main Methods:

    • A novel scheme estimates eyeball motions by minimizing rendered eyeball and image differences.
    • A 3D Taylor expansion-based linearization ensures robustness and real-time optimization.
    • An online bidirectional regression method handles eye occlusions using information from the opposite eye.

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    Main Results:

    • The method achieves robust and accurate 3D eye performance reconstruction.
    • It effectively handles variations in iris appearance, lighting, and highlights.
    • Real-time performance is maintained across diverse head, face, and eye motions.

    Conclusions:

    • The proposed method offers a significant advancement in real-time 3D eye performance reconstruction.
    • It provides a robust solution for capturing detailed eye movements under challenging conditions.
    • This technique can improve the realism of digital avatars and facial animation.