论文标题
历史重复：人类运动通过运动的注意预测
History Repeats Itself: Human Motion Prediction via Motion Attention
论文作者
论文摘要
人类运动预测旨在预测未来的人类姿势，鉴于过去的运动。无论是基于反复发作还是馈送神经网络，现有方法都无法模拟人类运动倾向于重演的观察，即使是为了复杂的运动动作和烹饪活动。在这里，我们介绍了一个基于注意力的前馈网络，该网络明确利用了这一观察结果。特别是，我们提出注意运动注意力以捕获当前运动上下文和历史运动子序列之间的相似性，而不是通过姿势相似性进行建模框架的注意。通过图卷积网络汇总相关的过去动作并处理结果，使我们能够有效利用长期历史的运动模式来预测未来的姿势。我们对人类360万，积聚和3DPW的实验证明了我们方法对期刊和非颗粒状作用的好处。多亏了我们的注意模型，它在所有三个数据集上都产生了最先进的结果。我们的代码可在https://github.com/wei-mao-2019/hisrepitself上找到。
Human motion prediction aims to forecast future human poses given a past motion. Whether based on recurrent or feed-forward neural networks, existing methods fail to model the observation that human motion tends to repeat itself, even for complex sports actions and cooking activities. Here, we introduce an attention-based feed-forward network that explicitly leverages this observation. In particular, instead of modeling frame-wise attention via pose similarity, we propose to extract motion attention to capture the similarity between the current motion context and the historical motion sub-sequences. Aggregating the relevant past motions and processing the result with a graph convolutional network allows us to effectively exploit motion patterns from the long-term history to predict the future poses. Our experiments on Human3.6M, AMASS and 3DPW evidence the benefits of our approach for both periodical and non-periodical actions. Thanks to our attention model, it yields state-of-the-art results on all three datasets. Our code is available at https://github.com/wei-mao-2019/HisRepItself.
