Exploring spatial-temporal dependencies from observed motions is one of the
core challenges of human motion prediction. Previous methods mainly focus on
dedicated network structures to model the spatial and temporal dependencies.
This paper considers a new direction by introducing a model learning framework
with auxiliary tasks. In our auxiliary tasks, partial body joints' coordinates
are corrupted by either masking or adding noise and the goal is to recover
corrupted coordinates depending on the rest coordinates. To work with auxiliary
tasks, we propose a novel auxiliary-adapted transformer, which can handle
incomplete, corrupted motion data and achieve coordinate recovery via capturing
spatial-temporal dependencies. Through auxiliary tasks, the auxiliary-adapted
transformer is promoted to capture more comprehensive spatial-temporal
dependencies among body joints' coordinates, leading to better feature
learning. Extensive experimental results have shown that our method outperforms
state-of-the-art methods by remarkable margins of 7.2%, 3.7%, and 9.4% in terms
of 3D mean per joint position error (MPJPE) on the Human3.6M, CMU Mocap, and
3DPW datasets, respectively. We also demonstrate that our method is more robust
under data missing cases and noisy data cases. Code is available at
this https URL

通过引入辅助任务的模型学习框架，本文提出了一种新的方向，即通过捕捉时空依赖关系以实现坐标恢复，以及通过辅助任务促使辅助自适应变压器更好地学习特征，并通过实验证明了该方法的优越性。