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MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
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Motion In-Betweening via Deep ∆-Interpolator.

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    Synthesizing human motion is more accurate using a deep learning interpolator in delta mode. This approach, referencing local frames, outperforms global reference methods for key frame-based motion generation.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Robotics

    Background:

    • Human motion synthesis is crucial for animation, robotics, and virtual reality.
    • Existing methods often struggle with accuracy and robustness, particularly when conditioned on sparse key frames.

    Purpose of the Study:

    • To develop a more accurate and effective deep learning-based method for synthesizing human motion from key frames.
    • To investigate the benefits of operating in a 'delta mode' for motion interpolation.

    Main Methods:

    • A deep learning interpolator was designed to operate in the delta mode.
    • The spherical linear interpolator (SLERP) was used as a baseline for comparison.
    • Experiments were conducted on publicly available human motion datasets.

    Main Results:

    • The delta mode interpolator achieved state-of-the-art performance on benchmark datasets.
    • The delta-regime was shown to be effective when referenced to the last known frame (zero-velocity model).

    Conclusions:

    • Operating in a local reference frame (delta mode) is more accurate and robust than using a global reference frame for motion synthesis.
    • The proposed deep learning approach offers significant improvements in human motion synthesis accuracy and effectiveness.