Human motion prediction is a complex task as it involves forecasting variables over time on a graph of connected sensors. This is especially true in the case of few-shot learning, where we strive to forecast motion sequences for previously unseen actions based on only a few examples. Despite this, almost all related approaches for few-shot motion prediction do not incorporate the underlying graph, while it is a common component in classical motion prediction. Furthermore, state-of-the-art methods for few-shot motion prediction are restricted to motion tasks with a fixed output space meaning these tasks are all limited to the same sensor graph. In this work, we propose to extend recent works on few-shot time-series forecasting with heterogeneous attributes with graph neural networks to introduce the first few-shot motion approach that explicitly incorporates the spatial graph while also generalizing across motion tasks with heterogeneous sensors. In our experiments on motion tasks with heterogeneous sensors, we demonstrate significant performance improvements with lifts from 10.4% up to 39.3% compared to best state-of-the-art models. Moreover, we show that our model can perform on par with the best approach so far when evaluating on tasks with a fixed output space while maintaining two magnitudes fewer parameters.

翻译：人体运动预测是一项复杂任务，因为它涉及在连接传感器构成的图上对随时间变化的变量进行预测。这在少样本学习场景中尤为突出，即仅基于少量示例对未见过的动作序列进行预测。尽管如此，几乎所有针对少样本运动预测的方法都未整合底层图结构，而该结构恰是经典运动预测中的常见组成部分。此外，当前最先进的少样本运动预测方法仅限于具有固定输出空间的运动任务，这意味着这些任务均受限于相同的传感器图。本文提出将近期关于具有异质属性的少样本时间序列预测的研究扩展至图神经网络，从而首次引入一种显式融入空间图结构、同时能跨异构传感器运动任务进行泛化的少样本运动方法。在针对异构传感器的运动任务实验中，与最优基准模型相比，我们的方法实现了10.4%至39.3%的性能提升。此外，结果表明，在评估固定输出空间的任务时，我们的模型在保持参数数量减少两个数量级的情况下，其性能可与当前最优方法相媲美。