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Gap Reconstruction in Optical Motion Capture Sequences Using Neural Networks.

Przemysław Skurowski¹, Magdalena Pawlyta^1,2

¹Department of Graphics, Computer Vision and Digital Systems, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.

Sensors (Basel, Switzerland)

|September 28, 2021

Summary

Optical motion capture data often has gaps. Simple neural networks can effectively fill these gaps in motion sequences, outperforming complex models, especially for longer recordings.

Keywords:

BILSTM FFNN GRU LSTM gap filling motion capture neural networks reconstruction

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Area of Science:

Biomechanics
Computer Vision
Machine Learning

Background:

Optical motion capture is widely used for acquiring motion data.
Recordings can contain errors and gaps due to technical limitations and marker occlusion.
Existing methods for gap-filling include interpolation and matrix completion.

Purpose of the Study:

To review neural network architectures for filling gaps in motion capture data.
To compare neural network performance against traditional methods.
To identify factors influencing the effectiveness of gap-filling techniques.

Main Methods:

Review of various neural network architectures applied to motion capture gap-filling.
Utilizing the FBM framework for body kinematic structure representation.
Comparison of neural networks with linear interpolation and matrix completion.

Main Results:

Simple linear feedforward neural networks can outperform complex architectures for longer motion sequences.
The effectiveness of neural networks may be influenced by the limited training data available.
Input sequence acceleration and monotonicity significantly impact gap-filling results.

Conclusions:

Neural networks offer a viable solution for optical motion capture gap-filling.
Simpler models can be surprisingly effective, challenging the need for highly complex architectures.
Understanding input sequence characteristics is crucial for optimizing motion data reconstruction.