  In the past few years, neural character animation has emerged and offered an
automatic method for animating virtual characters. Their motion is synthesized
by a neural network. Controlling this movement in real time with a user-defined
control signal is also an important task in video games for example. Solutions
based on fully-connected layers (MLPs) and Mixture-of-Experts (MoE) have given
impressive results in generating and controlling various movements with
close-range interactions between the environment and the virtual character.
However, a major shortcoming of fully-connected layers is their computational
and memory cost which may lead to sub-optimized solution. In this work, we
apply pruning algorithms to compress an MLP- MoE neural network in the context
of interactive character animation, which reduces its number of parameters and
accelerates its computation time with a trade-off between this acceleration and
the synthesized motion quality. This work demonstrates that, with the same
number of experts and parameters, the pruned model produces less motion
artifacts than the dense model and the learned high-level motion features are
similar for both
