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.

An image is a very effective tool for conveying emotions. Many researchers have investigated in computing the image emotions by using various features extracted from images. In this paper, we focus on two high level features, the object and the background, and assume that the semantic information of images is a good cue for predicting emotion. An object is one of the most important elements that define an image, and we find out through experiments that there is a high correlation between the object and the emotion in images. Even with the same object, there may be slight difference in emotion due to different backgrounds, and we use the semantic information of the background to improve the prediction performance. By combining the different levels of features, we build an emotion based feed forward deep neural network which produces the emotion values of a given image. The output emotion values in our framework are continuous values in the 2-dimensional space (Valence and Arousal), which are more effective than using a few number of emotion categories in describing emotions. Experiments confirm the effectiveness of our network in predicting the emotion of images.

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.

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.

In multi-user Redirected Walking (RDW), the space subdivision method divides a shared physical space into sub-spaces and allocates a sub-space to each user. While this approach has the advantage of precluding any collisions between users, the conventional space subdivision method suffers from frequent boundary resets due to the reduction of available space per user. To address this challenge, in this study, we propose a space subdivision method called Optimal Space Partitioning (OSP) that dynamically divides the shared physical space in real-time. By exploiting spatial information of the physical and virtual environment, OSP predicts the movement of users and divides the shared physical space into optimal sub-spaces separated with shutters. Our OSP framework is trained using deep reinforcement learning to allocate optimal sub-space to each user and provide optimal steering. Our experiments demonstrate that OSP provides higher sense of immersion to users by minimizing the total number of reset counts, while preserving the advantage of the existing space subdivision strategy: ensuring better safety to users by completely eliminating the possibility of any collisions between users beforehand. Our project is available at https://github.com/AppleParfait/OSP-Archive.