  In this work, we present MotionMixer, an efficient 3D human body pose
forecasting model based solely on multi-layer perceptrons (MLPs). MotionMixer
learns the spatial-temporal 3D body pose dependencies by sequentially mixing
both modalities. Given a stacked sequence of 3D body poses, a spatial-MLP
extracts fine grained spatial dependencies of the body joints. The interaction
of the body joints over time is then modelled by a temporal MLP. The
spatial-temporal mixed features are finally aggregated and decoded to obtain
the future motion. To calibrate the influence of each time step in the pose
sequence, we make use of squeeze-and-excitation (SE) blocks. We evaluate our
approach on Human3.6M, AMASS, and 3DPW datasets using the standard evaluation
protocols. For all evaluations, we demonstrate state-of-the-art performance,
while having a model with a smaller number of parameters. Our code is available
at: https://github.com/MotionMLP/MotionMixer
