  Despite the fact that neural networks are widely used for speech-driven head
motion synthesis, it is well-known that the output of neural networks is noisy
or discontinuous due to the limited capability of deep neural networks in
predicting human motion. Thus, post-processing is required to obtain smooth
head motion trajectories for animation. It is common to apply a linear filter
or consider keyframes as post-processing. However, neither approach is optimal
as there is always a trade-off between smoothness and accuracy. We propose to
employ a neural network trained in a way that it is capable of reconstructing
the head motions, in order to overcome this limitation. In the objective
evaluation, this filter is proved to be good at de-noising data involving types
of noise (dropout or Gaussian noise). Objective metrics also demonstrate the
improvement of the joined head motion's smoothness after being processed by our
proposed filter. A detailed analysis reveals that our proposed filter learns
the characteristic of head motions. The subjective evaluation shows that
participants were unable to distinguish the synthesised head motions with our
proposed filter from ground truth, which was preferred over the Gaussian filter
and moving average.
