We propose a low-asymmetry interface to improve the presence of non-head-mounted-display (non-HMD) users in shared virtual reality (VR) experiences with HMD users. The low-asymmetry interface ensures that the HMD and non-HMD users’ perception of the VR environment is almost similar. That is, the point-of-view asymmetry and behavior asymmetry between HMD and non-HMD users are reduced. Our system comprises a portable mobile device as a visual display to provide a changing PoV for the non-HMD user and a walking simulator as an in-place walking detection sensor to enable the same level of realistic and unrestricted physical-walking-based locomotion for all users. Because this allows non-HMD users to experience the same level of visualization and free movement as HMD users, both of them can engage as the main actors in movement scenarios. Our user study revealed that the low-asymmetry interface enables non-HMD users to feel a presence similar to that of the HMD users when performing equivalent locomotion tasks in a virtual environment. Furthermore, our system can enable one HMD user and multiple non-HMD users to participate together in a virtual world; moreover, our experiments show that the non-HMD user satisfaction increases with the number of non-HMD participants owing to increased presence and enjoyment.

This paper proposes a system for predicting increases in virtual world user actions. The virtual world user population is a very important aspect of these worlds; however, methods for predicting fluctuations in these populations have not been well documented. Therefore, we attempt to predict changes in virtual world user populations with deep learning, using easily accessible online data, including formal datasets from Google Trends, Wikipedia, and online communities, as well as informal datasets collected from online forums. We use the proposed system to analyze the user population of EVE Online, one of the largest virtual worlds.

We present a new 3D user interface for selecting an arbitrarily shaped region of interest (ROI) in virtual reality. The interface’s primary objective is the selection of a surface geometry’s boundaries having various shapes (e.g., arch- or concave-shaped boundaries) for 3D model editing or annotation. Compared with previous convex ROI shape selection, we explore a more challenging arbitrarily shaped selection. We address this issue by generating a flexible selection space based on a user’s multiple finger position mapping into the shaped ROI on a 3D surface geometry and providing visual feedback to assist the user in progressively refining the selection shape. We employ a leap motion controller for free-hand finger position input and gesture-based navigation to construct a flexible and natural user interface. To demonstrate the efficiency of our method, we evaluate the system usability by conducting a user study and compare the results with those of the state-of-the-art manual-input ROI selection technique.

Internet memes have become widely used by people for online communication and interaction, particularly through social media. Interest in meme-generation research has been increasing rapidly. In this study, we address the problem of meme generation as an image captioning task, which uses an encoder–decoder architecture to generate Chinese meme texts that match image content. First, to train the model on the characteristics of Chinese memes, we collected a dataset of 3,000 meme images with 30,000 corresponding humorous Chinese meme texts. Second, we introduced a Chinese meme generation system that can generate humorous and relevant texts from any given image. Our system used a pre-trained ResNet-50 for image feature extraction and a state-of-the-art transformer-based GPT-2 model to generate Chinese meme texts. Finally, we combined the generated text and images to form common image memes. We performed qualitative evaluations of the generated Chinese meme texts through different user studies. The evaluation results revealed that the Chinese memes generated by our model were indistinguishable from real ones.

Numerical Mathematics: Theory, Methods and Applications (NMTMA) publishes high-quality papers on the construction, analysis and application of numerical methods for solving scientific and engineering problems. Research and expository papers devoted to the numerical solution of mathematical equations arising in all areas of science and technology are expected. The journal originates from Numerical Mathematics: A Journal of Chinese Universities (English Edition), and has been sponsored by Nanjing University.