IEEE Computer Graphics and Applications

End-of-line tests and defect detection are vital for ensuring the reliability of electric motors. However, automated defect detection methods, e.g., data-driven approaches, face challenges due to the limited availability of real data from failed motors. Simulated data, though beneficial, lacks the complexity of real motors, impacting the performance of these methods when applied to actual observations. To tackle this challenge, we introduce a visual analysis tool designed to facilitate the analysis of measured and simulated data, presented in the form of time series data...

In this paper, we propose TraVis, an interactive system that allows users to explore and analyze complex traffic trajectory data at urban intersections. Trajectory data contains a large amount of spatio-temporal information, and while previous studies have mainly focused on the macroscopic aspects of traffic flow, TraVis employs visualization methods to investigate and analyze microscopic traffic events, i.e., high-value scenes in trajectory data. TraVis contains a novel view design and provides multiple interaction modalities to offer users the most intuitive insights into high-value scenes...

The commonly used analytic bidirectional reflectance distribution functions (BRDFs) do not model goniochromatism, that is, angle-dependent material color. The material color is usually defined by a diffuse reflectance spectrum or RGB vector and a specular part based on a spectral complex index of refraction. Extension of the commonly used BRDFs based on wave theory can help model goniochromatism, but this comes at the cost of significant added model complexity. We measured the goniochromatism of structual color pigments used for additive color printing and found that we can fit the observed spectral angular dependence of the bidirectional reflectance using a simple modification of the standard microfacet BRDF model...

Caricature generation aims to translate portrait photos into caricatures with exaggerated and hand-drawn artistic styles. Previous methods faced challenges in creating diverse and meaningful exaggeration effects, yielding unsatisfactory and uncontrollable results. To overcome this, we proposed ETCari, a novel weakly supervised exaggeration transfer network. ETCari enables the learning of diverse exaggeration caricature styles from various artists, better meeting individual customization requirements and achieving diversified exaggeration while retaining identity features...

Autonomous driving is no more a topic of science fiction. Advancement of autonomous driving technologies are now reliable. Effectively harnessing the information is essential for enhancing the safety, reliability, and efficiency of autonomous vehicles. In this article, we explore the pivotal role of visualization and visual analytics (VA) techniques used in autonomous driving. By employing sophisticated data visualization methods, VA, researchers and practitioners transform intricate datasets into intuitive visual representations, providing valuable insights for decision-making processes...

Results of automated detection of complex patterns in temporal data, such as trajectories of moving objects, may be not good enough due to the use of strict pattern specifications derived from imprecise domain concepts. To address this challenge, we propose a novel visual analytics approach that combines expert knowledge and automated pattern detection results to construct features that effectively distinguish patterns of interest from other types of behaviour. These features are then used to create interactive visualisations enabling a human analyst to generate labelled examples for building a feature-based pattern classifier...

We address the problem of modifying a given well-designed 2D sewing pattern to accommodate garment edits in the 3D space. Existing methods usually adjust the sewing pattern by applying uniform flattening to the 3D garment. The problems are twofold: first, it ignores local scaling of the 2D sewing pattern such as shrinking ribs of cuffs; second, it does not respect the implicit design rules and conventions of the industry, such as the use of straight edges for simplicity and precision in sewing. To address those problems, we present a pattern adjustment method that considers the non-uniform local scaling of the 2D sewing pattern by utilizing the intrinsic scale matrix...

The increasing demand for edge devices causes the necessity for recent technologies to be adaptable to nonspecialized hardware. In particular, in the context of augmented, virtual reality, and computer graphics, the 3-D object reconstruction task from a sparse point cloud is highly computationally demanding and for this reason, it is difficult to accomplish on embedded devices. In addition, the majority of earlier works have focused on mesh quality at the expense of speeding up the creation process. In order to find the best balance between time for mesh generation and mesh quality, we aim to tackle the object reconstruction process by developing a lightweight implicit representation...

Visualization for explainable and trustworthy machine learning remains one of the most important and heavily researched fields within information visualization and visual analytics with various application domains, such as medicine, finance, and bioinformatics. After our 2020 state-of-the-art report comprising 200 techniques, we have persistently collected peer-reviewed articles describing visualization techniques, categorized them based on the previously established categorization schema consisting of 119 categories, and provided the resulting collection of 542 techniques in an online survey browser...

This article examines the intricate relationship between humans and text-to-image generative models (generative artificial intelligence/genAI) in the realm of art. The article frames that relationship in the theory of mediated action-a well-established theory that conceptualizes how tools shape human thoughts and actions. The article describes genAI systems as learning, cocreating, and communicating, multimodally capable hybrid systems that distill and rely on the wisdom and creativity of massive crowds of people and can sometimes surpass them...

This study aimed to evaluate the performance of three artificial intelligence (AI) image synthesis models, Dall-E 2, Stable Diffusion, and Midjourney in generating urban design imagery based on scene descriptions. A total of 240 images were generated and evaluated by two independent professional evaluators using an adapted Sensibleness and Specificity Average (SSA) metric. The results showed significant differences between the three AI models, as well as differing scores across urban scenes, suggesting that some projects and design elements may be more challenging for AI art generators to represent visually...

We requested an interview with Jon McCormack after we encountered his work when looking for artists doing compelling work at the intersection of art and artificial intelligence (AI).

As mobile and wearable devices are becoming increasingly powerful, access to personal data is within reach anytime and anywhere. Currently, methods of data exploration while on-the-go and in-situ are, however, often limited to glanceable and micro visualizations, which provide narrow insight. In this article, we introduce the notion of databiting, the act of interacting with personal data to obtain richer insight through lightweight and transient exploration. We focus our discussion on conceptualizing databiting and arguing its potential values...

The Virtual Access to STEM Careers (VASC) project is an intertwined classroom and virtual reality (VR) curricular program for third through fourth graders. Elementary school students learn about and take on the roles and responsibilities of STEM occupations through authentic, problem-based tasks with physical kits and immersive VR environments. This article reports on a round of curriculum and virtual environment development and in-classroom experimentation that was guided by preliminary results gathered from our initial VASC prototyping and testing...

Recent developments in artificial intelligence (AI) and machine learning (ML) have led to the creation of powerful generative AI methods and tools capable of producing text, code, images, and other media in response to user prompts. Significant interest in the technology has led to speculation about what fields, including visualization, can be augmented or replaced by such approaches. However, there remains a lack of understanding about which visualization activities may be particularly suitable for the application of generative AI...

This article examines the choices between sitting and standing in virtual reality (VR) experiences, addressing conflicts, challenges, and opportunities. It explores issues such as the risk of motion sickness in stationary users and virtual rotations, the formation of mental models, consistent authoring, affordances, and the integration of embodied interfaces for enhanced interactions. Furthermore, it delves into the significance of multisensory integration and the impact of postural mismatches on immersion and acceptance in VR...

Traditional approaches to data visualization have often focused on comparing different subsets of data, and this is reflected in the many techniques developed and evaluated over the years for visual comparison. Similarly, common workflows for exploratory visualization are built upon the idea of users interactively applying various filter and grouping mechanisms in search of new insights. This paradigm has proven effective at helping users identify correlations between variables that can inform thinking and decision-making...

We introduce a workflow for the visual assessment of interobserver variability in medical image segmentation. Image segmentation is a crucial step in the diagnosis, prognosis, and treatment of many diseases. Despite the advancements in autosegmentation, clinical practice widely relies on manual delineations performed by radiologists. Our work focuses on designing a solution for understanding the radiologists' thought processes during segmentation and for unveiling reasons that lead to interobserver variability...

Computing occluding contours is often a crucial step in stroke-based artistic 3-D stylization for movies, video games, and visualizations. However, many existing applications use only simple curve stylization techniques, such as thin black lines or hand-animated strokes. This is because sophisticated procedural stylization requires accurate curve topology, which has long been an unsolved research problem. This article describes a recent theoretical breakthrough in the topology problem. Specifically, the new theory points out that existing contour algorithms often generate curves that cannot have any valid visibility, and new algorithms show how to correct the problem...

This article presents a visual analytics framework, idMotif, to support domain experts in identifying motifs in protein sequences. A motif is a short sequence of amino acids usually associated with distinct functions of a protein, and identifying similar motifs in protein sequences helps to predict certain types of disease or infection. idMotif can be used to explore, analyze, and visualize such motifs in protein sequences. We introduce a deep-learning-based method for grouping protein sequences and allow users to discover motif candidates of protein groups based on local explanations of the decision of a deep-learning model...