Enchanting Your Noodles: GAN-based Real-time Food-to-Food Translation and Its Impact on Vision-induced Gustatory Manipulation
Teams: 奈良先端科学技术大学院大学，日本电气通信大学和东京大学
Writers: Kizashi Nakano ; Daichi Horita ; Nobuchika Sakata ; Kiyoshi Kiyokawa ; Keiji Yanai ; Takuji Narumi
Publication date: Feb 2019
Abstract
We propose a novel gustatory manipulation interface which utilizes the cross-modal effect of vision on taste elicited with augmented reality (AR)-based real-time food appearance modulation using a generative adversarial network (GAN). Unlike existing systems which only change color or texture pattern of a particular type of food in an inflexible manner, our system changes the appearance of food into multiple types of food in real-time flexibly, dynamically and interactively in accordance with the deformation of the food that the user is actually eating by using GAN-based image-to-image translation. The experimental results reveal that our system successfully manipulates gustatory sensations to some extent and that the effectiveness depends on the original and target types of food as well as each user’s food experience.
