In this bachelors’ thesis we investigate and visualize a Gaussian
process latent variable model (GP-LVM), used to model
high dimensional motion capture data of a musical conductor
in a lower dimensional space.
This work expands upon the degree project of K. Karipidou,
”Modelling the body language of a musical conductor
using Gaussian Process Latent Variable Models”, in which
GP-LVMs are used to perform dimensionality reduction of
motion capture data of a conductor conducting a string
quartet, expressing four different underlying emotional interpretations
(tender, angry, passionate and neutral). In
Karipidou’s work, a GP-LVM coupled with K-means and
an HMM are used for classification of unseen conduction
motions into the aforementioned emotional interpretations.
We develop a graphical user interface (GUI) for visualizing
the resulting lower dimensional mapping performed
by a GP-LVM side by side with the motion capture data.
The GUI and the GP-LVM mapping is done within Matlab,
while the open source 3D creation suite Blender is used to
visualize the motion capture data in greater detail, which
is then imported into the GUI.
Furthermore, we develop a new GP-LVM in the same
manner as Karipidou, but based on the angles between the
motion capture nodes, and compare its accuracy in classifying
emotion to that of Karipidou’s location based model.
The evaluation of the GUI concludes that it is a very useful
tool when a GP-LVM is to be examined and evaluated.
However, our angle-based model does not improve the classification
result compared to Karipidou’s position-based.
Thus, using Euler angles are deemed inappropriate for this
Keywords: Gaussian process latent variable model,
motion capture, visualization, body language, musical conductor,
2016. , 38 p.