论文标题
几乎没有人类运动预测的异质传感器
Few-shot human motion prediction for heterogeneous sensors
论文作者
论文摘要
人类运动预测是一项复杂的任务，因为它涉及在连接传感器图上随时间推移的预测变量。在几乎没有学习的情况下，尤其如此，我们努力预测基于几个示例以前看不见的动作。尽管如此，几乎所有相关运动预测的相关方法都不包含基础图，而它是经典运动预测中的常见组成部分。此外，用于几次运动预测的最新方法仅限于具有固定输出空间的运动任务，这意味着这些任务都限于同一传感器图。在这项工作中，我们建议将最新的作品扩展到具有图形神经网络的异质属性的几个播放时间序列预测上，以引入第一个明确结合空间图的首个几次运动方法，同时还通过异构传感器跨运动任务概括。在我们对具有异质传感器的运动任务的实验中，与最佳最新模型相比，我们证明了从10.4％到39.3％的提升效果改善。此外，我们表明，当对具有固定输出空间的任务进行评估，同时保持两个幅度较少的参数时，我们的模型可以与最佳方法相同。
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.
