  This paper tackles the problem of human motion prediction, consisting in
forecasting future body poses from historically observed sequences.
State-of-the-art approaches provide good results, however, they rely on deep
learning architectures of arbitrary complexity, such as Recurrent Neural
Networks(RNN), Transformers or Graph Convolutional Networks(GCN), typically
requiring multiple training stages and more than 2 million parameters. In this
paper, we show that, after combining with a series of standard practices, such
as applying Discrete Cosine Transform(DCT), predicting residual displacement of
joints and optimizing velocity as an auxiliary loss, a light-weight network
based on multi-layer perceptrons(MLPs) with only 0.14 million parameters can
surpass the state-of-the-art performance. An exhaustive evaluation on the
Human3.6M, AMASS, and 3DPW datasets shows that our method, named siMLPe,
consistently outperforms all other approaches. We hope that our simple method
could serve as a strong baseline for the community and allow re-thinking of the
human motion prediction problem. The code is publicly available at
\url{https://github.com/dulucas/siMLPe}.
