We present a high-performance method for real-time relighting of high-fidelity volumetric clouds. Building on Relightable Neural Assets, we introduce key adaptations that enable neural shading of volumetric cloud phenomena within a rasterization-based pipeline. In particular, we incorporate a per-pixel thickness parameter to capture view-dependent opacity and replace the generalizing single light source with a sky illumination model, allowing the network to learn complex atmospheric scattering effects. To achieve real-time performance, we depart from density-field-based volumetric rendering and instead operate on mesh representations combined with a triplane feature encoding. This enables a fully rasterization-driven solution that reproduces volumetric appearance without requiring ray marching or volume integration. We further describe a complete pipeline for converting volumetric cloud assets into a neural representation trained from path-traced supervision. We evaluate our method through an ablation study analyzing both image quality and runtime performance. Our adaptations improve reconstruction quality from 18.13 dB to 22.32 dB while achieving rendering times as low as 3.7 ms per frame. These results demonstrate that our approach enables high-quality, relightable cloud rendering suitable for real-time and performance-critical applications.

Nonlinear dimensionality reduction (NLDR) is widely used to project high-dimensional data into low-dimensional embeddings for visualization and analysis. However, these embeddings are often difficult to interpret, particularly for non-expert users. Existing studies have mainly examined how dimensionality reduction results can be interpreted through conventional visualizations, while supporting NLDR understanding through immersive visualizations has received far less attention. In this paper, we present an immersive visualization system that features three immersive visual cues for interpreting NLDR embeddings: a feature attribution panel, local reliability highlighting, and neighbor relationship visualization. The system was implemented as a standalone application on a mobile VR device and evaluated through a within-subject user study with 19 participants. Based on their task performance and subjective questionnaire responses, the results show that the proposed visual cues support selected NLDR analysis tasks and improve perceived confidence in solving tasks, although the effects were not equally strong across all tasks. Overall, this work demonstrates the potential of mobile VR as a practical setting for human-centered, interpretation-oriented immersive analytics of NLDR results.

In this paper, we investigate the current state and development of personalized smart immersive extended reality environments (PSI-XR). PSI-XR has gained increasing traction across various fields such as education, entertainment, and healthcare, offering customized immersive experiences that address users’ personalized needs. This study performs a systematic literature review by collecting and analyzing related journal and conference papers in the domain. Following a comprehensive search across three databases, which yielded 1276 papers, a refined selection of 94 publications was made to conduct an in-depth analysis of cutting-edge research in the field of PSI-XR. This review focused on examining application domains, relevant technologies, and smart techniques, including artificial intelligence, with particular emphasis on advancements in personalization. The study provides insights into prospective advancements while also identifying the opportunities and challenges in this evolving field. This review is beneficial for both researchers and developers interested in exploring the state-of-the-art personalized perspective in a smart immersive extended reality environment.

This paper presents Geometry-Independent Path Planning (GIPP) in real time for games and other dynamic, interactive worlds using the GPU. The first contribution is the automatic generation of a geometry-independent 2D navigation mesh, termed GINT, at a user-defined resolution. GINT can be computed efficiently for complex scenes containing millions of triangles, without manual preprocessing. The second contribution is HALOS, a parallel, line-of-sight-inspired path planning algorithm that operates entirely on the GPU using GINT. Both GINT generation and HALOS execution are integrated into the GPU pipeline for optimal performance. GIPP scales well with large numbers of agents targeting the same goal and robustly adapts to dynamic environments, overcoming key limitations of many traditional path planning methods. Importantly, GIPP's real-time, high-resolution capabilities and GPU-based design make it especially valuable in virtual environments, where immersive, responsive, and large-scale agent navigation is crucial for presence, realism, and interactivity.

Integrating artificial intelligence (AI) with immersive analytics (IA) represents a promising means of leveraging advanced computational techniques to enhance data visualization and analysis. This study examines the state-of-the-art of AI-IA integration by addressing three key research issues: the significant application domains, the AI techniques used and their combinations, and current challenges and future directions. Results of reviewing 43 relevant studies reveal that AI-IA integration is still in its early stages, as existing research has mainly focused on a limited range of data types and application scenarios. By analyzing the application domains, this systematic literature review supports previous findings of important applications in the fields of education, manufacturing, and healthcare. At the same time, it identifies emerging applications that have progressed from XR and AI domains to AI-IA integration, such as sports events, assistive systems, urban planning, and disaster management. We contribute to extending established visual analytics (VA) pipelines into XR environments with integrated AI techniques. AI techniques are identified as contributing in five ways to this IA pipeline. Our contribution also includes identifying four key challenges and seven opportunities for future exploration. The review concludes that combining AI and IA holds the potential to create innovative applications using advanced AI and immersive visualization techniques. We present an overview of these applications and address key issues for future development.

This paper presents a conceptual design for a virtual reality (VR)-based furniture arrangement system, proposed to leverage immersive technologies and generative AI (GenAI) with intuitive interaction methods. The system utilizes a head-mounted display (HMD) to provide an immersive and intuitive user experience (UX) with multimodal interaction methods through hand and eye-tracking controls. Additionally, GenAl enables the virtual agent to engage in natural conversations with users and interpret and respond contextually, aiming to enhance personalization by understanding conversations. Users' interests and preferences are analyzed and predicted from the conversation and eye-gaze data, which provides recommendations for relevant furniture items and real-time personalized feedback. By combining these techniques, this design aims to create a seamless, interactive, intelligent, personalized virtual interior design and furniture arrangement experience within the immersive virtual environment (VE). The implementation of several key features demonstrates a proof of concept for our virtual furniture arrangement system.

The ”Human-Centered Intelligent Realities” (HINTS) laboratory is a strategic infrastructure project aiming to support research that advances the development of immersive, user-aware, and intelligent digital environments by integrating augmented reality (AR), virtual reality (VR), extended reality (XR), artificial intelligence (AI), and machine learning (ML). By combining virtual reality and communication-computing continuums, the HINTS environment seeks to create innovative concepts, methods, and tools that empower users to engage with digital systems in novel, efficient, and effective ways. Research in the HINTS laboratory focuses on experience assessment, new digital environments and interaction techniques, visual analytics, adaptive AI, and networking. This paper presents the HINTS laboratory, ongoing activities, and opportunities and challenges for the future.

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 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 visual quality by playing a fast-paced shooter game that requires many eye 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 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.

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.

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.

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.

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 rapid development of extended reality (XR) technologies such as augmented reality (AR) and virtual reality (VR). Further, there have been tremendous advancements in artificial intelligence (AI) and machine learning (ML). These two trends will have a significant impact on future digital societies. The vision of an immersive, ubiquitous, and intelligent virtual space opens up new opportunities for creating an enhanced digital world in which the users are at the center of the development process, so-called intelligent realities (IRs). The “Human-Centered Intelligent Realities” (HINTS) profile project will develop concepts, principles, methods, algorithms, and tools for human-centered IRs, thus leading the way for future immersive, user-aware, and intelligent interactive digital environments. The HINTS project is centered around an ecosystem combining XR and communication paradigms to form novel intelligent digital systems. HINTS will provide users with new ways to understand, collaborate with, and control digital systems. These novel ways will be based on visual and data-driven platforms which enable tangible, immersive cognitive interactions within real and virtual realities. Thus, exploiting digital systems in a more efficient, effective, engaging, and resource-aware condition. Moreover, the systems will be equipped with cognitive features based on AI and ML, which allow users to engage with digital realities and data in novel forms. This paper describes the HINTS profile project and its initial results.

This paper presents a novel Discrete Element Method (DEM) on the GPU for efficient snow simulation. To this end, our approach employs an iterative scheme on particles that easily allows the snow density to vary vastly for simulation while still maintaining a relatively large time step. We provide computationally inexpensive ways to capture cohesion and compression in the snow that enables us to generalize the behavior of various kinds of snow (like dry, wet, etc.) by varying physical parameters within the same simulator. We achieve a speed-up of nearly eight times with one million snow particles over the existing real-time method, even while dealing with scenes containing complex object boundaries. Furthermore, our simulator not only retains stability at these large time steps but also improves upon the physical behavior of the existing method. We have also conducted a user evaluation of our approach, where a majority of the participants voted in favor of its realism value for computer games.

AbstractThe field of autonomous vehicles is gaining wide recognition in the industry, academia as well as social media. 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 and autonomous vehicles. Secondly, based on the findings of this analysis, we developed a questionnaire to gauge 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.

This paper presents novel and efficient strategies to spatially adapt the amount of computational effort applied based on the local dynamics of a free surface flow, for both classic weakly compressible SPH (WCSPH) and predictive-corrective incompressible SPH (PCISPH). Using a convenient and readily parallelizable block-based approach, different regions of the fluid are assigned differing time steps and solved at different rates to minimize computational cost. Our approach for WCSPH scheme extends an asynchronous SPH technique from compressible flow of astrophysical phenomena to the incompressible free surface setting, and further accelerates it by entirely decoupling the time steps of widely spaced particles. Similarly, our approach to PCISPH adjusts the the number of iterations of density correction applied to different regions, and asynchronously updates the neighborhood regions used to perform these corrections; this sharply reduces the computational cost of slowly deforming regions while preserving the standard density invariant. We demonstrate our approaches on a number of highly dynamic scenarios, demonstrating that they can typically double the speed of a simulation compared to standard methods while achieving visually consistent results.