FrictGAN: Frictional Signal Generation from Fabric Texture Images using Generative Adversarial Network
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
The electrostatic tactile display could render the tactile feeling of different haptic texture surfaces by generating the frictional force through voltage modulation when a finger is sliding on the display surface. However, it is challenging to prepare and fine-tune the appropriate frictional signals for haptic design and texture simulation. We present FrictGAN, a deep-learningbased framework to synthesize frictional signals for electrostatic tactile displays from fabric texture images. Leveraging GANs (Generative Adversarial Networks), FrictGAN could generate the displacement-series data of frictional coefficients for the electrostatic tactile display to simulate the tactile feedback of fabric material. Our preliminary experimental results showed that FrictGAN could achieve considerable performance on frictional signal generation based on the input images of fabric textures.
Description
@inproceedings{10.2312:egve.20201254,
booktitle = {ICAT-EGVE 2020 - International Conference on Artificial Reality and Telexistence and Eurographics Symposium on Virtual Environments},
editor = {Argelaguet, Ferran and McMahan, Ryan and Sugimoto, Maki},
title = {{FrictGAN: Frictional Signal Generation from Fabric Texture Images using Generative Adversarial Network}},
author = {Cai, Shaoyu and Ban, Yuki and Narumi, Takuji and Zhu, Kening},
year = {2020},
publisher = {The Eurographics Association},
ISSN = {1727-530X},
ISBN = {978-3-03868-111-3},
DOI = {10.2312/egve.20201254}
}