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:人类运动预测是一项复杂的任务,因为它涉及在一个连接的传感器图上长期预测变量。 这一点在几个短镜头的学习中尤其如此。 我们努力根据几个例子预测先前不为人知的行动的动作序列。 尽管如此,几乎所有对短镜头运动预测的相关方法都不包含基本图表,而这是古典运动预测的一个共同组成部分。 此外,对短镜头运动预测的最先进方法仅限于固定输出空间的运动任务,这意味着这些任务都限于同一个传感器图。 在这项工作中,我们提议扩大最近关于以图形神经网络为不同属性的短镜头时序预测的工作,以引入第一个明确纳入空间图的短镜头运动方法,同时将不同传感器的移动任务加以概括。在对移动任务进行实验时,我们展示了显著的性能改进,从10.4%升至39.3%,而最佳的状态模型则比最佳的先进。 此外,我们表明,在用固定输出空间评价任务时,我们的模式可以与迄今为止的最佳方法相同地表现得分,同时保持两个星级的参数。