In recent years, virtual reality (VR) technology has advanced rapidly, with spatial audio playing a key role in enhancing immersion. The Head-Related Transfer Function (HRTF) is central to realistic three-dimensional soundscapes, significantly improving directional perception. However, challenges remain regarding users’ ability to accurately recall and comprehend auditory content in virtual environments, impacting overall audio intelligibility, recall, and immersion. This study conducted a 3 (VR scenario) × 3 (sound source type) × 3 (audio mode) factorial experiment with ninety participants to investigate effects on intelligibility, recall, and immersion. Results revealed that participants exhibited superior audio recall for static, moving, and rotating sound sources positioned on the left and right compared to other directions. Additionally, compared to simulated 5.1 and 7.1 surround sound systems, VR spatial audio significantly enhanced user immersion, intelligibility, and recall.

Abstract Virtual reality (VR) interaction safety is a prerequisite for all user activities in the virtual environment. While seeking a deep sense of immersion with little concern about surrounding obstacles, users may have limited ability to perceive the real‐world space, resulting in possible collisions with real‐world objects. Nowadays, recent works and rendering techniques such as the Chaperone can provide safety boundaries to users but confines them in a small static space and lack of immediacy. To solve this problem, we propose a dynamic approach based on user motion prediction named SCARF, which uses Spearman's correlation analysis, rule learning, and few‐shot learning to achieve prediction of user movements in specific VR tasks. Specifically, we study the relationship between user characteristics, human motion, and categories of VR tasks and provides an approach that uses biomechanical analysis to define the interaction space in VR dynamically.We report on a user study with 58 volunteers and establish a three dimensional kinematic dataset from a VR game. The experiments validate that our few‐shot learning model is effective and can improve the performance of motion prediction. Finally, we implement SCARF in VR environment for dynamic safety boundary adjustment.

In recent years, in order to solve the problem of deep learning in data deficient cities, especially the cold start problem. Researchers put forward a new idea: transfer the model and knowledge from data abundant cities to data scarce cities, also called urban transfer learning. However, in urban transfer learning, the cost for transferring different target cities and source cities cannot be known in advance. In other words, the effectiveness of urban transfer learning need to be improved. In order to solve this problem, we propose a general method for city similarity measurement in urban transfer learning. Through this method, we carry out transfer learning among the cities with higher degree of similarity, which obviously improve the effectiveness of transfer learning at the data level. At the same time, we have also effectively combined this city similarity measurement method with urban transfer learning, and demonstrated the relevant experiment results.

With the popularity of VR, the body interaction in VR has not received enough corresponding attention, and the interaction design that violates the law of the human body happens a lot. Therefore, we study the action interaction in virtual reality through motion arc, an important parameter of human action, to get the factors that affect the user’s body interaction in virtual reality. A within-subject experiment (n=18) was conducted, in which all participants played three VR games and responded to the post-game questionnaire. Through video recordings of the front and right side of them, 3D skeleton modeling was reconstructed by using OpenPose and the conversion relationship between the four coordinate systems under computer vision. After the results of operations such as skeleton standardization and motion segmentation, clustering algorithms are used to cluster similar users, and Spearman’s Rank Correlation Coefficient is used to study the influence of user characteristics on motion arc. Our results indicated that in an unconstrained game, the tutorial makes the motion arc increase, while in a constrained game, the change of motion arc is more complex. It is also found that the participants’ instructions learning has the greatest impact on the average motion arc, followed by individual factors (including age gender, etc.) and sports experience.

With the development of modern cities, multiple types and wide distribution of urban data has been gradually collected.Effectively using urban data to solve city development and planning issues has become a research hot-spot.Currently, the data scale in modern cities is quite different, and the fitting degree of machine learning algorithm based on single city is not mature yet.This paper studies the problem with transfer learning technique, and trains the prediction model of urban hotel development scale using multi-source city data.Based on the location data and related information of 15 different cities, the relevant knowledge is transferred, and a city feature extraction and similarity measurement framework is proposed.

User experience (UX) is a key factor that affects software survival time. A rich line of research has studied the relationships between UX and software factors to modify software and improve user satisfaction. However, the existing machine learning models for predicting UX on small data set is not accurate enough, and research with traditional statistical methods only obtained indistinct relations among UX, user characteristics and software factors. With the goal of improving the accuracy of UX model and obtaining sufficient UX relationships, we propose Transfer in Cart (TrCart) algorithm and Transfer Adaboost in Cart (TrAdaBoostCart) algorithm. To verify this approach, we present the UX study on a desktop game and an android game. According to the experimental results, we find that the TrAdaBoostCart has better accuracy and interpretable results. Hence, the proposed approach provides important guidelines for the design process of mobile applications.

International audience

International audience