  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.
