  In this work we propose a novel solution for 3D skeleton-based human motion
prediction. The objective of this task consists in forecasting future human
poses based on a prior skeleton pose sequence. This involves solving two main
challenges still present in recent literature; (1) discontinuity of the
predicted motion which results in unrealistic motions and (2) performance
deterioration in long-term horizons resulting from error accumulation across
time. We tackle these issues by using a compact manifold-valued representation
of 3D human skeleton motion. Specifically, we model the temporal evolution of
the 3D poses as trajectory, what allows us to map human motions to single
points on a sphere manifold. Using such a compact representation avoids error
accumulation and provides robust representation for long-term prediction while
ensuring the smoothness and the coherence of the whole motion. 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. Experiments have been conducted on CMU MoCap
and Human 3.6M datasets and demonstrate the superiority of our approach over
the state-of-the-art both in short and long term horizons. The smoothness of
the generated motion is highlighted in the qualitative results.
