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.

Numerous studies have previously explored the perception of horizontal movements. This includes research on Redirected Walking (RDW). However, the challenge of replicating the sensation of vertical movement has remained a recurring theme. Many conventional methods rely on physically mimicking steps or slopes, which can be hazardous and induce fear. This is especially true when head-mounted displays (HMDs) obstruct the user’s field of vision. Our primary objective was to reproduce the sensation of ascending a slope while traversing a flat surface. This effect is achieved by giving the users the haptic sensation of gripping a tilted handrail similar to those commonly found on ramps or escalators. To achieve this, we developed a walker-type handrail device capable of tilting across a wide range of angles. We induced a cross-modal effect to enhance the perception of walking up a slope. This was achieved by combining haptic feedback from the hardware with an HMD-driven visual simulation of an upward-sloping scene. The results indicated that the condition with tactile presentation significantly alleviated fear and enhanced the sensation of walking uphill compared to the condition without tactile presentation.

Gaze estimation technology has been widely used in the field of computer vision in recent years. It has been utilized in various fields, such as automotive systems, human-computer interaction and augmented reality (AR) / virtual reality (VR). In these applications, the target of gaze estimation is apparent, and sensors and cameras are placed at close distances to ensure precise gaze estimation. Conversely, applications such as measuring attention in advertisements have emerged, by utilizing gaze direction obtained from the increasing number of surveillance cameras in urban areas and public spaces. These surveillance cameras are not primarily intended for capturing individuals’ faces for gaze detection. Furthermore, potential obstructions, which may obscure individuals, create challenges that differ from the typical environments considered for gaze estimation. In this paper, we propose a method to estimate the gaze direction of a person with high accuracy, even in images taken by such surveillance cameras. We also compare and evaluate the proposed method using an existing dataset of surveillance camera images.

The aim of this study is to calculate optimal walking routes in real space for users partaking in immersive virtual reality (VR) games without compromising their immersion. To this end, we propose a navigation system to automatically determine the route to be taken by a VR user to avoid collisions with surrounding obstacles. The proposed method is evaluated by simulating a real environment. It is verified to be capable of calculating and displaying walking routes to safely guide users to their destinations without compromising their VR immersion. In addition, while walking in real space while experiencing VR content, users can choose between 6-DoF (six degrees of freedom) and 3-DoF (three degrees of freedom). However, we expect users to prefer 3-DoF conditions, as they tend to walk longer while using VR content. In dynamic situations, when two pedestrians are added to a designated computer-generated real environment, it is necessary to calculate the walking route using moving body prediction and display the moving body in virtual space to preserve immersion.

Entering a small space such as an elevator or a crowded train with a stranger can cause discomfort and suffocation. This is because the stranger is invading the individual’s personal space. However, it is difficult to maintain an appropriate interpersonal distance from others at all times in various situations. Therefore, a noise-canceling HMD [2][3] that uses AR to change the size of the person in the field of vision has been proposed as a means of reducing noise such as discomfort caused by inappropriate interpersonal distance. In this paper, we propose an improvement method using generative AI for background completion in noise-canceling HMDs.