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.

InteractionGAN: ImageLevel Interaction using Generative Adversarial Networks We present a novel strategy for imagelevel interaction that is applicable to a single image without any prior structural knowledge, such as object status or reconstructed 3D models. By training sets of input image and interaction pairs using a target image, our model can generate result images by applying the desired interaction to new unseen images. The proposed method is differentiated from previous approaches for changing poses, which requires absolute statuses for training images or estimated 3D model with reconstruction errors. Based on the conceptual analysis of encoderdecoder networks, we propose a novel generator network architecture containing a feature converter network, which is suitable for applying interactions to images. We also implement a discriminator network for training, which is a wellknown technique for generative adversarial networks. Experimental results demonstrate that the proposed method successfully generates result images with applied interactions without any prior knowledge. We expect that our method will provide insights into novel interaction schemes for augmented reality by reflecting interactions onto real scenes and providing more realistic user experiences.