# Vibrotactile Signal Generation from Texture Images or Attributes using Generative Adversarial Network

arXiv: https://arxiv.org/abs/1902.07480 · HTML: https://mlsanity.com/paper/1902.07480/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2019.

## Built on by (most cited first, 4 of 4)

- [Visual-Tactile Cross-Modal Data Generation using Residue-Fusion GAN with Feature-Matching and Perceptual Losses](https://mlsanity.com/paper/2107.05468/) (2021)
- [Development and Evaluation of a Learning-based Model for Real-time Haptic Texture Rendering](https://mlsanity.com/paper/2212.13332/) (2022)
- [The Language of Touch: Translating Vibrations into Text with Dual-Branch Learning](https://mlsanity.com/paper/2603.26804/) (2026)
- [Why Modeling Human Haptic Material Perception with AI Is Difficult](https://mlsanity.com/paper/2605.16602/) (2026)

## Abstract (arXiv)

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.