This study explores foveated rendering for its quality impact on players in virtual reality (VR) video game settings. Foveated rendering has the potential to decrease the performance cost by only rendering the part of the scene where the user is looking at a higher resolution, which it achieves with the use of an eye tracker. A user study is conducted to test the perceived visualquality by playing a fast-paced shooter game that requires manyeye and head movements using a head-mounted display (HMD). The game is played with three different types of foveation: no foveation, static, and dynamic foveated rendering. Results show that the majority of participants did not notice a difference in the visual quality between the foveated and non-foveated game versions.

This paper applies spatial and temporal adaptivity to an existing discrete element method (DEM) based snow simulation on the GPU. For spatial adaptivity, visually significant spatial regions are identified and simulated at varying resolutions. To this end, we propose efficient splitting and merging to generate adaptive resolution while maintaining the simulation stability. We obtain further optimization by skipping computation on temporally inactive regions. In agreement with the base solver, our method also operates almost entirely on the GPU, which includes operations like activity determination, merging, and splitting of the particles. We demonstrate that a speedup of three times or more of the original non-adaptive simulation can be achieved on scenes containing about 3 million particles. We also discuss the advantages and drawbacks of our spatiotemporal optimization in different simulation scenarios.

Snow and ice animation methods are becoming increasingly popular in the field of computer graphics (CG). The applications of snow and ice in CG are varied, ranging from generating realistic background landscapes to avalanches and physical interaction with objects in movies, games, etc. Over the past two decades, several methods have been proposed to capture the time-evolving physical appearance or simulation of snow and ice using different models at different scales. This state-of-the-art report aims to identify existing animation methods in the field, provide an up-to-date summary of the research in CG, and identify gaps for promising future work. Furthermore, we also attempt to identify the primarily related work done on snow and ice in some other disciplines, such as civil or mechanical engineering, and draw a parallel with the similarities and differences in CG.

This paper works towards an initial ontology of assessment techniques for building AI-enriched human-centered XR systems, denoted Intelligent Realities (IRs). Rather than connecting technologies, our work analyses the characteristics and requirements of IRs of being 'human-centered' and creates an ontology of techniques to assess and measure these features. To achieve this, we use an approach based on Formal Concept Analysis (FCA) to establish a concept hierarchy from a set of critical concepts in the area and their properties. The novel concept defines a metrology, i.e., a set of concepts and units of measurement that can be used to shape the architecture of human-centered XR and metaverse systems. Our work focuses particularly on the ethical and privacy needs of system design. 

Recent developments using extended reality (XR) technologies have allowed for increased use in healthcare in the last few years. This review paper explores how XR applications are utilized in home-based nursing education, in particular, to identify future challenges and opportunities. The systematic literature review evaluates relevant extracted papers based on publication information, XR technology used for education purposes, target users, and study design and evaluation, including sample size. The results show potential for using XR technologies in home-based nursing education. In particular, Virtual Reality (VR) has become quite popular and the most used to date. However, Augmented Reality (AR) has also emerged as an alternative for the future.

In this paper, we investigate the possibility of leveraging the predictive power of machine learning to generate animated lightning bolts in the image space efficiently. To this end, we selected state-of-the-art machine learning architectures based on Generative Adversarial Network (GAN) and trained them on the commonly available videos. We demonstrate that visually convincing animations are achievable even when employing a limited dataset. The visual realism of the generated sequences of lightning bolts is assessed by conducting a user study on the participants.

Artificial Intelligence (AI) embodied products are becoming ubiquitous in the modern world. Organizations are hence updating themselves to design and develop such products. In this paper, we aim at identifying enablers and barriers in designing such products across several sectors. Our analysis of a broad range of literature in this field allowed us to identify these enablers and barriers. We have developed SOTCUT and SEECUT models representing these enablers and barriers. We have discussed implication of the findings for the practice of designing AI-embodied products.

During the last decade, we have witnessed a rapiddevelopment of extended reality (XR) technologies such asaugmented reality (AR) and virtual reality (VR). Further, therehave been tremendous advancements in artificial intelligence(AI) and machine learning (ML). These two trends will havea significant impact on future digital societies. The vision ofan immersive, ubiquitous, and intelligent virtual space opensup new opportunities for creating an enhanced digital world inwhich the users are at the center of the development process,so-calledintelligent realities(IRs).The “Human-Centered Intelligent Realities” (HINTS) profileproject will develop concepts, principles, methods, algorithms,and tools for human-centered IRs, thus leading the wayfor future immersive, user-aware, and intelligent interactivedigital environments. The HINTS project is centered aroundan ecosystem combining XR and communication paradigms toform novel intelligent digital systems.HINTS will provide users with new ways to understand,collaborate with, and control digital systems. These novelways will be based on visual and data-driven platforms whichenable tangible, immersive cognitive interactions within realand virtual realities. Thus, exploiting digital systems in a moreefficient, effective, engaging, and resource-aware condition.Moreover, the systems will be equipped with cognitive featuresbased on AI and ML, which allow users to engage with digitalrealities and data in novel forms. This paper describes theHINTS profile project and its initial results. ©2023, Copyright held by the authors   

This article presents an interactive method for 3D cloud animation at the landscape scale by employing machine learning. To this end, we utilize deep convolutional generative adversarial network (DCGAN) on GPU for training on home-captured cloud videos and producing coherent animation frames. We limit the size of input images provided to DCGAN, thereby reducing the training time and yet producing detailed 3D animation frames. This is made possible through our preprocessing of the source videos, wherein several corrections are applied to the extracted frames to provide an adequate input training data set to DCGAN. A significant advantage of the presented cloud animation is that it does not require any underlying physics simulation. We present detailed results of our approach and verify its effectiveness using human perceptual evaluation. Our results indicate that the proposed method is capable of convincingly realistic 3D cloud animation, as perceived by the participants, without introducing too much computational overhead.

The field of autonomous vehicles is gaining wide recognition in the industry, academia as well as socialmedia. However, there is a lack of knowledge on expectations of people regarding this topic. To this end,this paper analyses extant research on perceptions of people in various countries about semi-autonomous andautonomous vehicles. Secondly, based on the findings of this analysis, we developed a questionnaire togauge the perceptions of the people in Sweden regarding such vehicles. The findings have important implications for the design of AVs in Sweden, and possibly other countries.