Adversarial gesture generation with realistic gesture phasing.

•We explore generative adversarial networks (GANs) for automatic speech gesture generation for virtual humans.•We define our training objective as a series of smaller sub-problems, including believable gesture dynamics with periods of both energy and acceleration, as well as pause.•To assess gesture dynamics, we train a classifier to automatically detect gesture phases, and we validate our results on a second speaker.