FrictGAN
FrictGAN generates frictional tactile signals from fabric texture images using generative adversarial networks. The project focuses on translating visual material appearance into friction cues that can be rendered through tactile displays for more realistic fabric simulation.
This approach contributes to data-driven texture rendering by enabling haptic systems to synthesize plausible tactile feedback from image inputs, supporting virtual shopping, material design, and immersive training scenarios.
Publication
Shaoyu Cai, Yuki Ban, Takuji Narumi, and Kening Zhu. "FrictGAN: Frictional Signal Generation from Fabric Texture Images using Generative Adversarial Network." International Conference on Artificial Reality and Telexistence & Eurographics Symposium on Virtual Environments, pp. 11-15. The Eurographics Association, 2020.
Shaoyu Cai, Lu Zhao, Yuki Ban, Takuji Narumi, Yue Liu, and Kening Zhu. "GAN-based Image-to-Friction Generation for Tactile Simulation of Fabric Material." Computers & Graphics, vol. 102, pp. 460-473, Feb. 2022.
Shaoyu Cai, Lu Zhao, Yuki Ban, Takuji Narumi, Yue Liu, and Kening Zhu. "GAN-based Image-to-Friction Generation for Tactile Simulation of Fabric Material." Computers & Graphics, vol. 102, pp. 460-473, Feb. 2022.