IEEE Computer Graphics and Applications最新文献

AI is the workhorse of modern data analytics and omnipresent across many sectors. Large language models and multimodal foundation models are today capable of generating code, charts, visualizations, etc. How will these massive developments of AI in data analytics shape future data visualizations and visual analytics workflows? What is the potential of AI to reshape methodology and design of future visual analytics applications? What will be our role as visualization researchers in the future? What are opportunities, open challenges, and threats in the context of an increasingly powerful AI? This Visualization Viewpoints discusses these questions in the special context of biomedical data analytics as an example of a domain in which critical decisions are taken based on complex and sensitive data, with high requirements on transparency, efficiency, and reliability. We map recent trends and developments in AI on the elements of interactive visualization and visual analytics workflows and highlight the potential of AI to transform biomedical visualization as a research field. Given that agency and responsibility have to remain with human experts, we argue that it is helpful to keep the focus on human-centered workflows, and to use visual analytics as a tool for integrating "AI-in-the-loop." This is in contrast to the more traditional term "human-in-the-loop." which focuses on incorporating human expertise into AI-based systems.

Generative artificial intelligence (AI) has immense potential to create diverse computer graphics for various applications, but it also raises significant ethical issues. This article examines the ethical landscape of using generative AI in computer graphics, highlighting key concerns, such as the authenticity of generated content, intellectual property rights, and cultural appropriation. Additional ethical challenges include algorithmic bias in graphics generation, representation, privacy, inclusivity, and the impact on human-computer interaction and artistic integrity. The displacement of creative professionals, erosion of trust in visual media, and psychological effects of AI-generated content further complicate the ethical debate. Addressing these issues requires a comprehensive approach that integrates technological innovation with regulatory oversight, ethical education, and collaboration among stakeholders. By carefully considering these ethical dimensions, we can fully leverage generative AI's potential in computer graphics while mitigating its risks.

The Japanese entertainment computer graphics (CG) industry, including games, animation, and visual effects, is facing a gap between industry demands and the programs being offered by educational institutions. As work is being diversified, this gap is becoming more apparent owing to the lack of education in specialized technical and communication skills that companies require. To address this gap between educational institutions and Japan's CG industry, we propose a new educational model developed through industry-academic collaboration. Centered on a "salad bowl" education framework, the model reflects corporate culture and project characteristics, and it incorporates a mentorship system that promotes the transfer of practical skills, meeting the needs of both students and companies. The initial implementation of the model revealed significant improvement in student skills and satisfaction, indicating the formation of the foundation necessary for workplace success. The study focuses on the training of CG creators in the industry and educational institutions, excluding computational knowledge and programming skills. This approach highlights the importance of long-term cooperation between education and industry in meeting diverse demands. Future research should explore the long-term impact and scalability of this model.

Augmented reality (AR) is emerging as the next ubiquitous wearable technology and is expected to significantly transform various industries in the near future. There has been tremendous investment in developing AR eyeglasses in recent years, including about $45 billion investment by Meta since 2021. Despite such efforts, the existing displays are very bulky in form factor and there has not yet been a socially acceptable eyeglasses-style AR display. Such wearable display eyeglasses promise to unlock enormous potential in diverse applications such as medicine, education, navigation, and many more; but until eyeglass-style AR glasses are realized, those possibilities remain only a dream. My research addresses this problem and makes progress "towards everyday-use augmented reality eyeglasses" through computational imaging, displays, and perception. My dissertation (Chakravarthula, 2021) made advances in three key and seemingly distinct areas: first, digital holography and advanced algorithms for compact, high-quality, true 3-D holographic displays; second, hardware and software for robust and comprehensive 3-D eye tracking via Purkinje Images; and third, automatic focus adjusting AR display eyeglasses for well-focused virtual and real imagery, toward potentially achieving 20/20 vision for users of all ages.

Data visualization methodologies were intensively leveraged during the COVID-19 pandemic. We review our design experience working on a set of interdisciplinary COVID-19 pandemic projects. We describe the challenges we met in these projects, characterize the respective user communities, the goals and tasks we supported, and the data types and visual media we worked with. Furthermore, we instantiate these characterizations in a series of case studies. Finally, we describe the visual analysis lessons we learned, considering future pandemics.

Constructivist learning is based on the principle that learners construct knowledge based on their prior knowledge and experiences. We explored the impact of a constructivist approach to introduce students to the Treemaps visualization technique. We developed software that helps students understand Treemaps using a synchronized, multiview, interactive node-link representation of the same data. While students in both groups-the ones who used the node-link diagram with the Treemaps and the ones who used only the interactive Treemaps-demonstrated significant improvement in learning, students who only interacted with the Treemaps representation performed better on a variety of tasks related to reading and interpreting Treemaps.

Advancements in virtual reality (VR) technology have enabled its use to assist in multiple fields. This study introduces a comprehensive framework designed to support exposure therapy through a series of VR serious games and physiological monitoring. It relies on a generic architecture, allowing for the modification of VR stimuli according to different phobias (e.g., arachnophobia, acrophobia), while the remaining modules can be reused for data collection and analysis. Furthermore, the framework incorporates customizable biofeedback mechanisms that trigger specific events or adjust stimulus levels based on physiological responses. Prior to integration into the overall architecture, the proposed VR serious games underwent assessment in various events with a total of 56 participants. In addition, the framework's ability to capture diverse biosignals and synchronize them with other relevant metrics was evaluated through two user studies involving a total of 23 participants.

Extended reality (XR) is evolving rapidly, offering new paradigms for human-computer interaction. This position paper argues that integrating large language models (LLMs) with XR systems represents a fundamental shift toward more intelligent, context-aware, and adaptive mixed-reality experiences. We propose a structured framework built on three key pillars: first, perception and situational awareness, second, knowledge modeling and reasoning, and third, visualization and interaction. We believe leveraging LLMs within XR environments enables enhanced situational awareness, real-time knowledge retrieval, and dynamic user interaction, surpassing traditional XR capabilities. We highlight the potential of this integration in neurorehabilitation, safety training, and architectural design while underscoring ethical considerations, such as privacy, transparency, and inclusivity. This vision aims to spark discussion and drive research toward more intelligent, human-centric XR systems.

Technological advancements are prompting the digitization of many industries, including fashion. Many brands are exploring ways to enhance customers' experience [e.g., offering new shopping-oriented services like virtual fitting rooms (VFRs)]. However, there are still challenges that prevent customers from effectively using these tools for trying on digital garments. Challenges are associated with difficulties in obtaining high-fidelity reconstructions of body shapes and providing realistic visualizations of animated clothes following real-time customers' movements. This article tackles such lacks by proposing a semiautomated pipeline supporting the creation of VFR experiences by exploiting state-of-the-art techniques for the accurate description and reconstruction of customers' 3-D avatars, motion capture-based animation, as well as realistic garment design and simulation. A user study in which the resulting VFR experience was compared with those created with two existing tools showed the benefits of the devised solution in terms of usability, embodiment, model accuracy, perceived value, adoption, and purchase intention.

Fiber tracking is a powerful technique that provides insight into the brain's white matter structure. Despite its potential, the inherent uncertainties limit its widespread clinical use. These uncertainties potentially hamper the clinical decisions neurosurgeons have to make before, during, and after the surgery. Many techniques have been developed to visualize uncertainties; however, there is limited evidence to suggest whether these uncertainty visualization influences neurosurgical decision-making. In this article, we evaluate the hypothesis that uncertainty visualization in fiber tracking influences neurosurgeon's decisions and confidence in their decisions. For this purpose, we designed a user study through an online interactive questionnaire and evaluate the influence of uncertainty visualization in neurosurgical decision-making. The results of this study emphasize the importance of uncertainty visualization in clinical decision-making by highlighting the influence of different intervals of uncertainty visualization in critical clinical decisions.