  Synthesizing natural head motion to accompany speech for an embodied
conversational agent is necessary for providing a rich interactive experience.
Most prior works assess the quality of generated head motion by comparing them
against a single ground-truth using an objective metric. Yet there are many
plausible head motion sequences to accompany a speech utterance. In this work,
we study the variation in the perceptual quality of head motions sampled from a
generative model. We show that, despite providing more diverse head motions,
the generative model produces motions with varying degrees of perceptual
quality. We finally show that objective metrics commonly used in previous
research do not accurately reflect the perceptual quality of generated head
motions. These results open an interesting avenue for future work to
investigate better objective metrics that correlate with human perception of
quality.
