Recently, considerable research has focused on personal assistant robots, and robots capable of rich human-like communication are expected. Among humans, non-verbal elements contribute to effective and dynamic communication. However, people use a wide range of diverse gestures, and a robot capable of expressing various human gestures has not been realized. In this study, we address human behavior modeling during interaction using a deep generative model. In the proposed method, to consider interaction motion, three factors, i.e., interaction intensity, time evolution, and time resolution, are embedded in the network structure. Subjective evaluation results suggest that the proposed method can generate high-quality human motions.

近来，大量的研究集中在个人助理机器人上，并且期望能够进行类似于人类的丰富通信的机器人。在人类中，非语言元素有助于有效和动态的交流。然而，人们使用各种各样的手势，并且尚未实现能够表达各种人类手势的机器人。在这项研究中，我们使用深度生成模型解决了交互过程中的人类行为建模。在所提出的方法中，为了考虑相互作用，将相互作用强度，时间演化和时间分辨率三个因素嵌入网络结构中。主观评估结果表明，该方法可以产生高质量的人体运动。