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Robust Non-Rigid Motion Tracking and Surface Reconstruction Using $L_0$ Regularization.

Kaiwen Guo, Feng Xu, Yangang Wang

    IEEE Transactions on Visualization and Computer Graphics
    |April 4, 2017
    PubMed
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    This study introduces a novel motion tracking technique for robustly reconstructing non-rigid motions from depth sensor data. The method enhances accuracy and prevents failures by leveraging articulate motion constraints.

    Area of Science:

    • Computer Vision
    • Robotics
    • Biomechanical Engineering

    Background:

    • Accurate non-rigid motion tracking is crucial for applications like human-computer interaction and medical analysis.
    • Existing methods struggle with complex motions, occlusions, and consumer-grade depth sensor limitations.

    Purpose of the Study:

    • To develop a robust and accurate motion tracking technique for non-rigid geometries using single-view depth input.
    • To improve the reconstruction of human-related motions by incorporating articulate motion constraints.

    Main Methods:

    • A novel motion regularizer with an iterative solver is proposed to constrain local deformations within articulate structures.
    • The technique integrates online extraction of articulate joint information to correct tracking errors.

    Related Experiment Videos

  • This joint information is iteratively used to enhance tracking accuracy and prevent failures.

    • Main Results:

    • The approach demonstrates substantially improved robustness and accuracy in motion tracking experiments.
    • Successful reconstruction of complex human body motions, including those with occlusions, facial, and hand movements.
    • Reduced solution space and physically plausible deformations are achieved through implicit articulation constraints.

    Conclusions:

    • The proposed motion tracking technique offers a significant advancement in reconstructing non-rigid motions from depth data.
    • Leveraging articulate motion subspaces provides a powerful mechanism for enhancing tracking performance and reliability.
    • The method shows great potential for real-world applications requiring precise motion analysis.