Learning expressive human-like head motion sequences from speech

With the development of new trends in human-machine interfaces, animated feature films and video games, better avatars and virtual agents are required that more accurately mimic how humans communicate and interact. Gestures and speech are jointly used to express intended messages. The tone and energy of the speech, facial expression, rigid head motion and hand motion combine in a non-trivial manner as they unfold in natural human interaction. Given that the use of large motion capture datasets is expensive and can only be applied in planned scenarios, new automatic approaches are required to synthesize realistic animation that capture and resemble the complex relationship between these communicative channels. One useful and practical approach is the use of acoustic features to generate gestures, exploiting the link between gestures and speech. Since the shape of the lips is determined by the underlying articulation, acoustic features have been used to generate visual visemes that match the spoken sentences [4, 5, 12, 17]. Likewise, acoustic features have been used to synthesize facial expressions [11, 30], exploiting the fact that the same muscles used for articulation also affect the shape of the face [44, 46]. One important gesture that has received less attention than other aspects in facial animations is rigid head motion. Head motion is important not only to acknowledge active listening or replace verbal information (e.g. “nod”), but also for many aspect of human