Predicting future motion based on historical motion sequence is a fundamental
problem in computer vision, and it has wide applications in autonomous driving
and robotics. Some recent works have shown that Graph Convolutional
Networks(GCN) are instrumental in modeling the relationship between different
joints. However, considering the variants and diverse action types in human
motion data, the cross-dependency of the spatio-temporal relationships will be
difficult to depict due to the decoupled modeling strategy, which may also
exacerbate the problem of insufficient generalization. Therefore, we propose
the Spatio-Temporal Gating-Adjacency GCN(GAGCN) to learn the complex
spatio-temporal dependencies over diverse action types. Specifically, we adopt
gating networks to enhance the generalization of GCN via the trainable adaptive
adjacency matrix obtained by blending the candidate spatio-temporal adjacency
matrices. Moreover, GAGCN addresses the cross-dependency of space and time by
balancing the weights of spatio-temporal modeling and fusing the decoupled
spatio-temporal features. Extensive experiments on Human 3.6M, AMASS, and 3DPW
demonstrate that GAGCN achieves state-of-the-art performance in both short-term
and long-term predictions.

提出了一种名为 Spatio-Temporal Gating-Adjacency GCN (GAGCN) 的运动预测模型，采用门控网络增强了 GCN 对于多种动作类型的推广能力，并通过平衡空间和时间的交叉依赖关系，融合了模型的特征，实现了对于人体运动预测的短期和长期性能的最先进水平。

基于时空门限 - 邻接图卷积网络的人体运动预测

Spatio-Temporal Gating-Adjacency GCN for Human Motion Prediction

Close human-robot cooperation is a key enabler for new developments in
advanced manufacturing and assistive applications. Close cooperation require
robots that can predict human actions and intent, and understand human
non-verbal cues. Recent approaches based on neural networks have led to
encouraging results in the human action prediction problem both in continuous
and discrete spaces. Our approach extends the research in this direction. Our
contributions are three-fold. First, we validate the use of gaze and body pose
cues as a means of predicting human action through a feature selection method.
Next, we address two shortcomings of existing literature: predicting multiple
and variable-length action sequences. This is achieved by introducing an
encoder-decoder recurrent neural network topology in the discrete action
prediction problem. In addition, we theoretically demonstrate the importance of
predicting multiple action sequences as a means of estimating the stochastic
reward in a human robot cooperation scenario. Finally, we show the ability to
effectively train the prediction model on a action prediction dataset,
involving human motion data, and explore the influence of the model's
parameters on its performance. Source code repository:
this https URL

通过神经网络方法，基于人类非语言行为，实现机器人对人类行动的预测，具有重要的应用价值。我们通过特征选择等手段，扩展了现有研究，设计出一种可以预测多个、不同长度动作序列的编码 - 解码循环神经网络拓扑结构，并论证了预测多个动作序列对协作场景奖励的估算十分重要。最后，在包含人类动作数据的动作预测数据集上进行有效训练，并探究了参数对性能的影响。