IEEE Computer Graphics and Applications最新文献

Data visualization provides intuitive and practical tools for information exploration and scientific discovery. However, with the increased availability of computing resources and sensing devices, data's ever-increasing size and complexity pose fundamental challenges to existing visualization techniques. The first challenge is data understanding, requiring new methodologies to extract key features and insights from large-scale data. Second, the development of data transmission and storage systems is outpaced by unprecedented data growth. This disparity challenges in situ data processing since data need to be transferred to a commodity workstation to conduct interactive inspections. Third, visualization tools and methodologies for understanding the uncertainties of scientific simulations are lacking. The author's research aims to address these challenges by significantly enriching topology-based visualization methodologies and tools for scientific data exploration. The author's dissertation (Yan, 2022) made advances in three areas: redefining topology for domain-specific features for data understanding, enhancing data reduction with topology for data transmission and storage, and developing methodologies for statistical feature analysis to mitigate uncertainty in data visualization. These methodologies and tools have applications in structural biology, climate science, combustion study, and neuroscience.

This case study explores a novel approach to data visualization that integrates statistical data with qualitative narratives, guided by the principles of data feminism and data humanism. The narrative, titled "Paths in Education," focuses on secondary education in Uruguay, a country where almost half of young adults have not completed mandatory education. Through a combination of quantitative analysis and interviews, the narrative is presented on an interactive scrollytelling website. This article discusses the critical visualization theories that inspired the chosen approach, the methodologies used for data collection and analysis, and the design process. It also discusses how the project relates to data feminism and humanism, as well as some lessons learned.

This article examines whether visualizations in cultural heritage fields can move beyond their traditional roles of exploration and analysis to facilitate higher order cognitive processes central to the practice of "critique." Drawing on arts and humanities perspectives, we define critical cognition as a mode of inquiry that explores and evaluates the potentiality of cultural objects and topics-and interrogates the associated representation and research systems that shape them-against the background of their actual state. We propose a framework for thinking about the critical potential of visualization systems and outline design strategies that enhance critical cognition and encourage responsive action. To that end, we examine multiple system components, including culture and context, collection practices, digital modeling, visualization, and research practices. We argue that by incorporating critical perspectives on these system components into interface design, we can empower users, foster more reflective and nuanced engagement with cultural collections, deepen understanding of their social, historical, and epistemic contexts, and ultimately strengthen trust in visualization methods within the arts and humanities.

The interdependencies of food, energy, and water (FEW) systems create a nexus opportunity to explore the strengths and vulnerabilities of individual and cross-sector interactions within FEW systems. However, the variables quantifying nexus interactions are hard to observe, which hinders the cross-sector analysis. To overcome such challenges, we present FEWSim, a visual analytics framework designed to support domain experts in exploring and interpreting simulation results from a coupled FEW model. FEWSim employs a three-layer asynchronous architecture: the model layer integrates food, energy, and water models to simulate the FEW nexus; the middleware layer manages scenario configuration and execution; and the visualization layer provides interactive visual exploration of simulated time-series results across FEW sectors. The visualization layer further facilitates the exploration across multiple scenarios and evaluates scenario differences in performance using sustainability indices of the FEW nexus. We demonstrate the utility of FEWSim through a case study for the Phoenix Active Management Area (AMA) in Arizona.

Technological innovations are reshaping the development of animation production. As virtual characters are increasingly used in animation creation and smart assistants, a key challenge is how to automatically generate dialogue gestures. However, current approaches often overlook a wide range of modalities and their interactions, resulting in gestures that have low contextual variation and noticeable jitter. To address these issues, we propose SSGesture, a novel diffusion-based framework that effectively captures cross-modal associations. Our three-layer attention structure enhances multimodal processing. We propose the first method to automatically resolve style conflicts through interpolation-based gesture style control, while implementing a unified unmarked style prompt structure via the PAAN layer. Our framework is practically applied in the field of intelligent virtual assistants to generate gestures in human animation synthesis and to realize various new applications. Extensive experiments and user studies have demonstrated that our proposed framework, provides substantial assistance in enhancing the efficiency of human animation production.

This project aims to investigate how cartoon and comic images can be better explored within the context of unsupervised image-to-image translation. Specifically, we seek to study the translation of anime illustrations into their manga representations, given a manga book as a reference. Although current state-of-the-art image-to-image translation models can convert images between different domains, existing methods for translating illustrations to manga style are scarce. We propose to exploit the unique characteristics of anime and manga images, allowing for a preliminary output that can support the translation process in two stages. We believe this approach can reduce model complexity while generating high-fidelity outputs. Furthermore, we aim to impose minimal restrictions on the manga target domain, making the translation fully unsupervised. Finally, the proposed framework's output can be used to produce rich datasets composed of colored and synthetic manga images, which would support colorization methods that rely on large amounts of paired training data.

We introduce the concept of a meta-object, a next-generation virtual object that inherits the form, properties, and functions of its real-world counterpart, enabling seamless synchronization, interaction, and sharing between the physical and virtual worlds. While plenty of today's virtual objects provide some sensory feedback and dynamic behavior, meta-objects fully integrate interactive and multisensory features within a structured data framework to enable real-time immersive experiences in a postmetaverse intelligent simulation platform. Three key components underpin the utilization of meta-objects in the postmetaverse: property-embedded modeling for physical and action realism, adaptive multisensory feedback tailored to user interactions, and a scene graph-based intelligence simulation platform for scalable and efficient ecosystem integration. By leveraging meta-objects through wearable augmented reality/virtual reality devices, the postmetaverse facilitates seamless interactions that transcend spatial and temporal barriers, paving the way for a transformative reality-virtuality convergence.

This article advances the notion of "data-inflected visions" to show how various visual representations may come to be imagined as data, and how doing so opens up different meanings for the political and affective work of data. The visuality of social issues is produced through competing hegemonic and alternative visions, and conventional visualization is not the only format in which data participate in visual contestation. Focusing on Latin American actions to visibilizar feminicide, I propose an encounter with activist-made imagery to elucidate how data participate in alternative representations of the issue. The article contributes both an exploration of the role of data in constructing how feminicide is seen and a novel approach to study data and visuality, to inspire scholars from visual studies and from feminist and critical data and data visualization studies to engage with images beyond conventional graphic representation as sites for the political and affective work of data.

This article discusses considerations on how visualization can be best positioned to help respond to future pandemics. We examine visualization, along with the corresponding and necessary enabling technologies and platforms, as a tool to facilitate a rapid and effective response to a forthcoming pandemic. We consider challenges in terms of an infrastructure supporting world-wide response, corresponding training and stakeholder engagement, integration of future technologies, and appraisal of such systems. Finally, we discuss how addressing these challenges also helps emergency response beyond infectious diseases.

This work investigates personal perspectives in visualization annotations as devices for collective data-driven storytelling. Inspired by existing efforts in critical cartography, we show how people share personal memories in a visualization of COVID-19 data and how comments by other visualization readers influence the reading and understanding of visualizations. Analyzing interaction logs, reader surveys, visualization annotations, and interviews, we find that reader annotations help other viewers relate to other people's stories and reflect on their own experiences. Further, we found that annotations embedded directly into the visualization can serve as social traces guiding through a visualization and help readers contextualize their own stories. With that, they supersede the attention paid to data encodings and become the main focal point of the visualization.