论文标题
使用关键的长期运动预测
Long Term Motion Prediction Using Keyposes
论文作者
论文摘要
长期的人类运动预测在安全至关重要的应用中至关重要，例如人类机器人相互作用和自主驾驶。在本文中，我们表明，要实现长期预测，就不需要在每次瞬间预测人的姿势。取而代之的是，通过插值关键来预测一些关键和近似中间的关键，更有效。 我们证明，我们的方法使我们能够预测将来最多5秒钟的现实动作，这比文献中典型的1秒长得多。此外，由于我们概率地对未来的关键建模，因此我们可以通过在推理时进行采样来产生多个合理的未来运动。在这个延长的时间段中，我们的预测比那些最先进的方法产生的预测更现实，更多样化，并且更好地保留运动动力学。
Long term human motion prediction is essential in safety-critical applications such as human-robot interaction and autonomous driving. In this paper we show that to achieve long term forecasting, predicting human pose at every time instant is unnecessary. Instead, it is more effective to predict a few keyposes and approximate intermediate ones by interpolating the keyposes. We demonstrate that our approach enables us to predict realistic motions for up to 5 seconds in the future, which is far longer than the typical 1 second encountered in the literature. Furthermore, because we model future keyposes probabilistically, we can generate multiple plausible future motions by sampling at inference time. Over this extended time period, our predictions are more realistic, more diverse and better preserve the motion dynamics than those state-of-the-art methods yield.
