Locomotion is an essential factor for interaction in virtual environments. Virtual reality allows users to move freely from the constraints of gravity or the environment, which is impossible in the real world. These experiences, in which users can move at all 6 degrees of freedom, can enable new navigation paradigms that exceed 2D ground-based movement in terms of enjoyment and interactivity. However, most existing VR locomotion interfaces have limitations because they are developed for ground-based locomotion, constraining the degrees of freedom (DoF) during movement as in the real world. This exploratory study was designed to seek the features required for three-dimensional (3D) locomotion by evaluating three types of interfaces: Slider, Teleport, and Point-Tug, which were implemented based on existing ground-based locomotion techniques. We then conducted a user study with 3D navigation tasks, using both objective and subjective measures to evaluate efficiency, overall usability, motion sickness, perceived workload, and presence. The results suggest that Slider has an advantage compared to the others in terms of usability and perceived workload since it can freely and intuitively designate the direction of movement.

Image-to-image translation methods have progressed from only considering the image-level information to integrating the global- and instance-level information. However, only the foreground instances are refined, and the background semantics are taken as an entire feature, which causes a substantial loss of the semantic information in the translation. Additionally, the insufficient quality of the translated semantic regions also leads to an unsatisfactory performance of the object recognition or visual odometry tasks in which the translated images/videos are further used. In this paper, we propose a novel generative adversarial network for panoptic-level image-to-image translation (PanopticGAN). The proposed method has three advantages: (1) the extracted panoptic perception (i.e., the foreground instances and background semantic regions) as content codes are aligned with the sampled panoptic style codes, which considers the panoptic-level information to avoid the semantic information loss, and the latent space of each object has a rich fusion of content and style codes to generate the higher-fidelity results; (2) a feature masking module is proposed to extract the representations within each object contour by masks for sharpening the object boundaries; (3) the improved fidelity of the translated semantic regions further contributes to enhancing the performance of the object recognition or visual odometry tasks that the translated images/videos are used in. In this paper, we also annotate a compact panoptic segmentation dataset for the thermal-to-color translation task. Extensive experiments are conducted to demonstrate the effectiveness of our PanopticGAN over the latest methods.

Water is an essential substance for humans in their daily lives. There are many opportunities for us to come in contact with water, such as cooking, bathing, and swimming. However, few studies have reproduced the sensation of water touching the skin. This study aims to propose a novel midair haptic device, named FlowHaptics, that reproduces the feeling of the force of flowing water over human fingers using multiple air jets. We first estimated the temporal pressure distribution change of water in two-dimensional space using machine-learning-accelerated fluid simulation. We controlled the airflow based on the pressure distribution change obtained from the fluid simulation to reproduce the feeling of flowing water over the fingers using our proposed device, which can control multiple air jets in real time. We performed a psycho-physical evaluation of different flow velocities and a subjective evaluation of different velocity profiles. We found that FlowHaptics reliably created the illusion of the pressure distribution of flowing water on the fingers where the flow velocity could be distinguished within the range of 8.42% to 13.05%, and our estimated flow velocity profile with the configuration of three air jets felt more similar to flowing water when compared to a constant velocity profile according to the users.

Most existing locomotion devices that represent the sensation of walking target a user who is actually performing a walking motion. Here, we attempted to represent the walking sensation, especially a kinesthetic sensation and advancing feeling (the sense of moving forward) while the user remains seated. To represent the walking sensation using a relatively simple device, we focused on the force rendering and its evaluation of the longitudinal friction force applied on the sole during walking. Based on the measurement of the friction force applied on the sole during actual walking, we developed a novel friction force display that can present the friction force without the influence of body weight. Using performance evaluation testing, we found that the proposed method can stably and rapidly display friction force. Also, we developed a virtual reality (VR) walk-through system that is able to present the friction force through the proposed device according to the avatar's walking motion in a virtual world. By evaluating the realism, we found that the proposed device can represent a more realistic advancing feeling than vibration feedback.