Redirection Strategy Switching: Selective Redirection Controller for Dynamic Environment Adaptation.

In this paper, we present the Selective Redirection Controller (SRC), which selects the optimal redirection controller based on the physical and virtual environment in Redirected Walking (RDW). The primary advantage of SRC over existing controllers is its dynamic switching among four different redirection controllers (S2C, TAPF, ARC, and SRL) based on the user's environment, as opposed to using a single fixed controller throughout the experience. By switching between redirection controllers based on the context around the user, SRC aims to optimize the advantages of each redirection strategy. The SRC model is trained using reinforcement learning to dynamically and instantaneously switch redirection controllers based on the user's environment. We evaluated the performance of SRC against traditional redirection controllers through simulations and user studies conducted in various physical and virtual environments. The findings indicate that SRC reduces the number of resets significantly compared to traditional redirection controllers. Heat map visualization was utilized during the development process to analyze which redirection controller SRC chooses based on the different environments around the user. SRC alternates between redirection techniques based on the user's environment, maximizing the advantages of each strategy for a superior RDW experience.

Locomotion Techniques for Dynamic Environments: Effects on Spatial Knowledge and User Experiences.

Various locomotion techniques are used to navigate and find way through space in virtual environments (VE), and each technique provides different experiences and performances to users. Previous studies have primarily focused on static environments, whereas there is a need for research from a different perspective of dynamic environments because there are many moving objects in VE, such as other users. In this study, we compare the effects of different locomotion techniques on the user's spatial knowledge and experience, depending on whether the virtual objects are moving or not. The investigated locomotion techniques include joystick, teleportation, and redirected walking (RDW), all commonly used for VR navigation. The results showed that the differences in spatial knowledge and user experience provided by different locomotion techniques can vary depending on whether the environment is static or dynamic. Our results also showed that for a given VE, there are different locomotion techniques that induce fewer collisions between the user and other objects, or reduce the time it takes the user to perform a given task. This study suggests that when designing a locomotion interface for a specific VR application, it is possible to improve the user's spatial knowledge and experience by recommending different locomotion techniques depending on the degree of environment dynamism and and type of task.

F-RDW: Redirected Walking With Forecasting Future Position.

In order to serve better VR experiences to users, existing predictive methods of Redirected Walking (RDW) exploit future information to reduce the number of reset occurrences. However, such methods often impose a precondition during deployment, either in the virtual environment's layout or the user's walking direction, which constrains its universal applications. To tackle this challenge, we propose a mechanism F-RDW that is twofold: (1) forecasts the future information of a user in the virtual space without any assumptions by using the conventional method, and (2) fuse this information while maneuvering existing RDW methods. The backbone of the first step is an LSTM-based model that ingests the user's spatial and eye-tracking data to predict the user's future position in the virtual space, and the following step feeds those predicted values into existing RDW methods (such as MPCRed, S2C, TAPF, and ARC) while respecting their internal mechanism in applicable ways. The results of our simulation test and user study demonstrate the significance of future information when using RDW in small physical spaces or complex environments. We prove that the proposed mechanism significantly reduces the number of resets and increases the traveled distance between resets, hence augmenting the redirection performance of all RDW methods explored in this work. Our project and dataset are available at https://github.com/YonseiCGnA-VR/F-RDW.

MARR : A Multi-Agent Reinforcement Resetter for Redirected Walking.

The reset technique of Redirected Walking (RDW) forcibly reorients the user's direction overtly to avoid collisions with boundaries, obstacles, or other users in the physical space. However, excessive resetting can decrease the user's sense of immersion and presence. Several RDW studies have been conducted to address this issue. Among them, much research has been done on reset techniques that reduce the number of resets by devising reset direction rules or optimizing them for a given environment. However, existing optimization studies on reset techniques have mainly focused on a single-user environment. In a multi-user environment, the dynamic movement of other users and static obstacles in the physical space increase the possibility of resetting. In this study, we propose Multi-Agent Reinforcement Resetter (MARR), which resets the user taking into account both physical obstacles and multi-user movement to minimize the number of resets. MARR is trained using multi-agent reinforcement learning to determine the optimal reset direction in different environments. This approach allows MARR to effectively account for different environmental contexts, including arbitrary physical obstacles and the dynamic movements of other users in the same physical space. We compared MARR to other reset technologies through simulation tests and user studies, and found that MARR outperformed the existing methods. MARR improved performance by learning the optimal reset direction for each subtle technique used in training. MARR has the potential to be applied to new subtle techniques proposed in the future. Overall, our study confirmed that MARR is an effective reset technique in multi-user environments.

"To be or Not to be Me?": Exploration of Self-Similar Effects of Avatars on Social Virtual Reality Experiences.

The growing interest in the self-similarity effect of avatars in virtual reality (VR) has spurred the creation of realistic avatars that closely mirror their users. However, despite extensive research on the self-similarity effect in single-user VR environments, our understanding of its impact in social VR settings remains underdeveloped. This shortfall exists despite the unique socio-psychological phenomena arising from the illusion of embodiment that could potentially alter these effects. To fill this gap, this paper provides an in-depth empirical investigation of how avatars' self-similarity influences social VR experiences. Our research uncovers several notable findings: 1) A high level of avatar self-similarity boosts users' sense of embodiment and social presence but has minimal effects on the overall presence and even slightly hinders immersion. These results are driven by increased self-awareness. 2) Among various factors that contribute to the self-similarity of avatars, voice stands out as a significant influencer of social VR experiences, surpassing other representational factors. 3) The impact of avatar self-similarity shows negligible differences between male and female users. Based on these findings, we discuss the pros and cons of incorporating self-similarity into social VR avatars. Our study serves as a foundation for further research in this field.

Studying the Effects of Congruence of Auditory and Visual Stimuli on Virtual Reality Experiences.

Studies in virtual reality (VR) have introduced numerous multisensory simulation techniques for more immersive VR experiences. However, although they primarily focus on expanding sensory types or increasing individual sensory quality, they lack consensus in designing appropriate interactions between different sensory stimuli. This paper explores how the congruence between auditory and visual (AV) stimuli, which are the sensory stimuli typically provided by VR devices, affects the cognition and experience of VR users as a critical interaction factor in promoting multisensory integration. We defined the types of (in)congruence between AV stimuli, and then designed 12 virtual spaces with different types or degrees of congruence between AV stimuli. We then evaluated the presence, immersion, motion sickness, and cognition changes in each space. We observed the following key findings: 1) there is a limit to the degree of temporal or spatial incongruence that can be tolerated, with few negative effects on user experience until that point is exceeded; 2) users are tolerant of semantic incongruence; 3) a simulation that considers synesthetic congruence contributes to the user's sense of immersion and presence. Based on these insights, we identified the essential considerations for designing sensory simulations in VR and proposed future research directions.

Future-Frame Prediction for Fast-Moving Objects with Motion Blur.

We propose a deep neural network model that recognizes the position and velocity of a fast-moving object in a video sequence and predicts the object's future motion. When filming a fast-moving subject using a regular camera rather than a super-high-speed camera, there is often severe motion blur, making it difficult to recognize the exact location and speed of the object in the video. Additionally, because the fast moving object usually moves rapidly out of the camera's field of view, the number of captured frames used as input for future-motion predictions should be minimized. Our model can capture a short video sequence of two frames with a high-speed moving object as input, use motion blur as additional information to recognize the position and velocity of the object, and predict the video frame containing the future motion of the object. Experiments show that our model has significantly better performance than existing future-frame prediction models in determining the future position and velocity of an object in two physical scenarios where a fast-moving two-dimensional object appears.

Motion Sickness Prediction in Stereoscopic Videos using 3D Convolutional Neural Networks.

In this paper, we propose a three-dimensional (3D) convolutional neural network (CNN)-based method for predicting the degree of motion sickness induced by a 360° stereoscopic video. We consider the user's eye movement as a new feature, in addition to the motion velocity and depth features of a video used in previous work. For this purpose, we use saliency, optical flow, and disparity maps of an input video, which represent eye movement, velocity, and depth, respectively, as the input of the 3D CNN. To train our machine-learning model, we extend the dataset established in the previous work using two data augmentation techniques: frame shifting and pixel shifting. Consequently, our model can predict the degree of motion sickness more precisely than the previous method, and the results have a more similar correlation to the distribution of ground-truth sickness.

Robust copyright protection using multiple ownership watermarks.

Generally, conventional transform (DWT and DFT, etc.) -based watermarking techniques provide only one spectrum plane for embedding the watermark, thus the embedding watermark information can be easily removed. To solve this problem, we propose an efficient cellular automata (CA) based watermarking method that CA transform (CAT) with various gateway values can provide many transform planes for watermark embedding according to various CA rules. In this paper, multiple ownership watermarks are first recorded in the form of an elemental image array (EIA), simultaneously, and then the recorded EIA as the watermark data is embedded into the CAT coefficient. An additional advantage of this proposed method is that EIA is composed of many elemental images and each elemental image has its own property of watermarks. Even though most data of elemental images are lost, the watermarks can be reconstructed from the remaining elemental images successfully. Experimental results show that the proposed technique provides good image quality and is robust in varying degree to some image processing attacks.

The squash-and-stretch stylization for character motions.

The squash-and-stretch describes the rigidity of the character. This effect is the most important technique in traditional cartoon animation. In this paper, we introduce a method that applies the squash-and-stretch effect to character motion. Our method exaggerates the motion by sequentially applying the spatial exaggeration technique and the temporal exaggeration technique. The spatial exaggeration technique globally deforms the pose in order to make the squashed or stretched pose by modeling it as a covariance matrix of joint positions. Then, the temporal exaggeration technique computes a time-warping function for each joint, and applies it to the position of the joint allowing the character to stretch its links appropriately. The motion stylized by our method is a sequence of squashed and stretched poses with stretching limbs. By performing a user survey, we prove that the motion created using our method is similar to that used in 2D cartoon animation and is funnier than the original motion for human observers who are familiar with 2D cartoon animation.