Food Talks: Visual and Interaction Principles for Representing Environmental and Nutritional Food Information in Augmented Reality This usercentered design research project aimed to investigate visual and interaction principles for augmented reality (AR) in the context of environmental and nutritional food labelling. Nutritional information on existing food labels is often misunderstood and environmental information is seldom depicted, despite consumer demand. The project explored the potential of AR in this context with a twophase process. Phase 1 aimed to engage large audiences in public spaces using a standalone AR device showing environmental information only. This allowed design strategies to be tested. Phase 2 addressed personalised information, combining nutritional and environmental data with smartphone AR. Here we integrated design learnings from Phase 1 and then focused on assessing the benefits of AR. A betweensubjects study with 84 participants compared twoversions of the smartphone application; one version showed the information with AR and the other showed the same information with a static page. Results showed that participants using the AR version learned more about food products than those using the static version. In addition, the AR version matched the high scores of the static version with regards to usability (SUS score of 86) and aesthetics (VisAWI score of 5.9), despite technical limitations of AR. This work reveals that AR can be a credible medium in the food industry and provides visual and interaction design learnings to inform designers in the industry.

DeepTaste: Augmented Reality Gustatory Manipulation with GANBased RealTime FoodtoFood Translation We have been studying augmented reality (AR)based gustatory manipulation interfaces and previously proposed a gustatory manipulation interface using generative adversarial network (GAN)based real time imagetoimage translation. Unlike threedimensional (3D) food modelbased systems that only change the color or texture pattern of a particular type of food in an inflexible manner, our GANbased system changes the appearance of food into multiple types of food in real time flexibly, dynamically, and interactively. In the present paper, we first describe in detail a user study on a visioninduced gustatory manipulation system using a 3D food model and report its successful experimental results. We then summarize identified problems of the 3D modelbased system and describe implementation details of the GANbased system. We finally report in detail the main user study in which we investigated the impact of the GANbased system on gustatory sensations and food recognition when somen noodles were turned into ramen noodles or fried noodles, and steamed rice into curry and rice or fried rice. The experimental results revealed that our system successfully manipulates gustatory sensations to some extent and that the effectiveness seems to depend on the original and target types of food as well as the experience of each individual with the food.