利用生成对抗网络从纹理图像或属性生成振动触觉信号
Vibrotactile Signal Generation from Texture Images or Attributes using Generative Adversarial Network
摘要：
提供与虚拟纹理表面的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，手动调整这种振动触觉刺激的纹理的每一个状态需要很多时间。因此，我们提出了一种新的方法来创建模型，实现从纹理图像或属性自动生成振动触觉。在本文中，我们首次尝试利用深度生成对抗训练的力量来生成振动触觉刺激。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，用户不能区分生成的信号和真实的，用户感觉生成的信号的真实性。因此，我们的模型可以提供适当的振动，根据纹理图像或它们的属性。我们的方法是适用于任何情况下，用户触摸的各种表面在一个预定义的方式。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
