Augmented reality (AR) marker hiding is a technique to visually remove AR markers in a real-time video stream. A conventional approach transforms a background image with a homography matrix calculated on the basis of a camera pose and overlays the transformed image on an AR marker region in a real-time frame, assuming that the AR marker is on a planar surface. However, this approach may cause discontinuities in textures around the boundary between the marker and its surrounding area when the planar surface assumption is not satisfied. This paper proposes a method for AR marker hiding without discontinuities around texture boundaries even under nonplanar background geometry without measuring it. For doing this, our method estimates the dense motion in the marker's background by analyzing the motion of sparse feature points around it, together with a smooth motion assumption, and deforms the background image according to it. Our experiments demonstrate the effectiveness of the proposed method in various environments with different background geometries and textures.

Vision-induced gustatory manipulation interfaces can help people with dietary restrictions feel as if they are eating what they want by modulating the appearance of the alternative foods they are eating in reality. However, it is still unclear whether vision-induced gustatory change persists beyond a single bite, how the sensation changes over time, and how it varies among individuals from different cultural backgrounds. The present paper reports on a user study conducted to answer these questions using a generative adversarial network (GAN)-based real-time image-to-image translation system. In the user study, 16 participants were presented somen noodles or steamed rice through a video see-through head mounted display (HMD) both in two conditions; without or with visual modulation (somen noodles and steamed rice were translated into ramen noodles and curry and rice, respectively), and brought food to the mouth and tasted it five times with an interval of two minutes. The results of the experiments revealed that vision-induced gustatory manipulation is persistent in many participants. Their persistent gustatory changes are divided into three groups: those in which the intensity of the gustatory change gradually increased, those in which it gradually decreased, and those in which it did not fluctuate, each with about the same number of participants. Although the generalizability is limited due to the small population, it was also found that non-Japanese and male participants tended to perceive stronger gustatory manipulation compared to Japanese and female participants. We believe that our study deepens our understanding and insight into vision-induced gustatory manipulation and encourages further investigation.

Non-photorealistic rendering (NPR) creates images with artistic styles of paintings. In this field, a number of methods of converting photographed images into non-photorealistic ones have been developed, and can be categorized into filter-based and exemplar-based approaches. In this paper, we focus on the exemplar-based approach and propose a novel method which transfers a style of a reference pictorial image to a photographed image. Specifically, we first input a pair of target and reference images. The target image is converted by minimizing an energy function which is defined based on the difference in intensities between an output image and a target image, and the pattern dissimilarity between an output image and a reference image. The proposed method transfers structures and colors of textures in the reference image and generates continuous textures by minimizing the energy function. In experiments, we demonstrate the effectiveness of the proposed method using a variety of images and examine the influence of parameter changes and intensity adjustment for pre-processing on resultant images.