Vibrotactile Signal Generation from Texture Images or Attributes Using Generative Adversarial Network
Vibrotactile Signal Generation from Texture Images or Attributes Using Generative Adversarial Network
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DOI：
10.1007/978-3-319-93399-3_3
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
Yusuke Ujitoko;Yuki Ban
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文献类型：
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作者：
Yusuke Ujitoko;Yuki Ban
提供与虚拟纹理表面状态相对应的振动触觉反馈，允许用户感知它们的触觉特性。然而，为纹理的每个状态手动调整这种振动触觉刺激需要花费很多时间。因此，我们提出了一种基于纹理图像或属性的振动触觉自动生成模型的新方法。在本文中，我们首次尝试利用深度生成对抗训练的力量来产生振动触觉刺激。具体来说，我们使用条件生成对抗网络（GANs）来实现在表面上移动笔时产生振动。初步的用户研究表明，用户无法区分生成的信号和真实的信号，用户对生成的信号有真实感。因此，我们的模型可以根据纹理图像或其属性提供适当的振动。我们的方法适用于用户以预定义的方式触摸各种表面的任何情况。
Providing vibrotactile feedback that corresponds to the state of the virtual texture surfaces allows users to sense haptic properties of them. However, hand-tuning such vibrotactile stimuli for every state of the texture takes much time. Therefore, we propose a new approach to create models that realize the automatic vibrotactile generation from texture images or attributes. In this paper, we make the first attempt to generate the vibrotactile stimuli leveraging the power of deep generative adversarial training. Specifically, we use conditional generative adversarial networks (GANs) to achieve generation of vibration during moving a pen on the surface. The preliminary user study showed that users could not discriminate generated signals and genuine ones and users felt realism for generated signals. Thus our model could provide the appropriate vibration according to the texture images or the attributes of them. Our approach is applicable to any case where the users touch the various surfaces in a predefined way.