  Producing realistic character animations is one of the essential tasks in
human-AI interactions. Considered as a sequence of poses of a humanoid, the
task can be considered as a sequence generation problem with spatiotemporal
smoothness and realism constraints. Additionally, we wish to control the
behavior of AI agents by giving them what to do and, more specifically, how to
do it. We proposed a spatiotemporally-conditioned GAN that generates a sequence
that is similar to a given sequence in terms of semantics and spatiotemporal
dynamics. Using LSTM-based generator and graph ConvNet discriminator, this
system is trained end-to-end on a large gathered dataset of gestures,
expressions, and actions. Experiments showed that compared to traditional
conditional GAN, our method creates plausible, realistic, and semantically
relevant humanoid animation sequences that match user expectations.
