  Human motion prediction aims to forecast future human poses given a prior
pose sequence. The discontinuity of the predicted motion and the performance
deterioration in long-term horizons are still the main challenges encountered
in current literature. In this work, we tackle these issues by using a compact
manifold-valued representation of human motion. Specifically, we model the
temporal evolution of the 3D human poses as trajectory, what allows us to map
human motions to single points on a sphere manifold. To learn these
non-Euclidean representations, we build a manifold-aware Wasserstein generative
adversarial model that captures the temporal and spatial dependencies of human
motion through different losses. Extensive experiments show that our approach
outperforms the state-of-the-art on CMU MoCap and Human 3.6M datasets. Our
qualitative results show the smoothness of the predicted motions.
