  We present a GAN-based Transformer for general action-conditioned 3D human
motion generation, including not only single-person actions but also
multi-person interactive actions. Our approach consists of a powerful
Action-conditioned motion TransFormer (ActFormer) under a GAN training scheme,
equipped with a Gaussian Process latent prior. Such a design combines the
strong spatio-temporal representation capacity of Transformer, superiority in
generative modeling of GAN, and inherent temporal correlations from the latent
prior. Furthermore, ActFormer can be naturally extended to multi-person motions
by alternately modeling temporal correlations and human interactions with
Transformer encoders. To further facilitate research on multi-person motion
generation, we introduce a new synthetic dataset of complex multi-person combat
behaviors. Extensive experiments on NTU-13, NTU RGB+D 120, BABEL and the
proposed combat dataset show that our method can adapt to various human motion
representations and achieve superior performance over the state-of-the-art
methods on both single-person and multi-person motion generation tasks,
demonstrating a promising step towards a general human motion generator.
