期刊
NEURAL NETWORKS
卷 132, 期 -, 页码 521-531出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2020.09.019
关键词
Human robot interaction; Human motion modeling; Generative Adversarial Networks; Human behavior during dialog
资金
- JST ERATO [JPMJER1401, 19H05693]
- Grants-in-Aid for Scientific Research [19H05693] Funding Source: KAKEN
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. (c) 2020 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据