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Now showing 1 - 10 of 11
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    Evaluating Bloom's Taxonomy-based Learning Modules for Parallel Coordinates Literacy
    (The Eurographics Association, 2022) Peng, Ilena; Firat, Elif E.; Laramee, Robert S.; Joshi, Alark; Bourdin, Jean-Jacques; Paquette, Eric
    In this paper, we present the results of an intervention designed to introduce parallel coordinates to students. The intervention contains six new modules inspired by Bloom's taxonomy that featured a combination of videos, tests, and tasks. We studied the impact of our modules with a corrective feedback mechanism inspired by Mastery Learning. Based on analyzing the data of our students, we found that students in the Corrective Immediate Feedback (CIF) group performed better on average on all the modules as compared to the students in the No Feedback (NF) group. In the tasks where students were required to construct parallel coordinates plots, students in the Corrective Immediate Feedback group produced plots with appropriate use of color, labels, legends, etc. Overall, students in both groups grew more confident in their ability to recognize parallel coordinates plots and expressed high confidence in their ability to interpret, create, and use parallel coordinates plots for data exploration and presentation in the future.
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    Time Series AMR Data Representation for Out-of-core Interactive Visualization
    (The Eurographics Association, 2022) Alexandre-Barff, Welcome; Deleau, Hervé; Sarton, Jonathan; Ledoux, Franck; Lucas, Laurent; Sauvage, Basile; Hasic-Telalovic, Jasminka
    Time-varying Adaptive Mesh Refinement (AMR) data have become an essential representation for 3D numerical simulations in many scientific fields. This observation is even more relevant considering that the data volumetry has increased significantly, reaching petabytes, hence largely exceeding the memory capacities of the most recent graphics hardware. Therefore, the question is how to access these massive data - AMR time series in particular - for interactive visualization purposes, without cracks, artifacts or latency. In this paper, we present a time-varying AMR data representation to enable a possible fully GPU-based out-of-core approach. We propose to convert the input data initially expressed as regular voxel grids into a set of AMR bricks uniquely identified by a 3D Hilbert's curve and store them in mass storage.
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    A First Step Towards the Inference of Geological Topological Operations
    (The Eurographics Association, 2022) Pascual, Romain; Belhaouari, Hakim; Arnould, Agnès; Le Gall, Pascale; Sauvage, Basile; Hasic-Telalovic, Jasminka
    Procedural modeling enables building complex geometric objects and scenes in a wide panel of applications. The traditional approach relies on the sequential application of a reduced set of construction rules. We offer to automatically generate new topological rules based on an initial object and the expected result of the future operation. Non-expert users can thereby develop their own operations. We exploited our approach for the modeling of the geological subsoil.
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    A Comprehensive Review of Data-Driven Co-Speech Gesture Generation
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Nyatsanga, Simbarashe; Kucherenko, Taras; Ahuja, Chaitanya; Henter, Gustav Eje; Neff, Michael; Bousseau, Adrien; Theobalt, Christian
    Gestures that accompany speech are an essential part of natural and efficient embodied human communication. The automatic generation of such co-speech gestures is a long-standing problem in computer animation and is considered an enabling technology for creating believable characters in film, games, and virtual social spaces, as well as for interaction with social robots. The problem is made challenging by the idiosyncratic and non-periodic nature of human co-speech gesture motion, and by the great diversity of communicative functions that gestures encompass. The field of gesture generation has seen surging interest in the last few years, owing to the emergence of more and larger datasets of human gesture motion, combined with strides in deep-learning-based generative models that benefit from the growing availability of data. This review article summarizes co-speech gesture generation research, with a particular focus on deep generative models. First, we articulate the theory describing human gesticulation and how it complements speech. Next, we briefly discuss rule-based and classical statistical gesture synthesis, before delving into deep learning approaches. We employ the choice of input modalities as an organizing principle, examining systems that generate gestures from audio, text and non-linguistic input. Concurrent with the exposition of deep learning approaches, we chronicle the evolution of the related training data sets in terms of size, diversity, motion quality, and collection method (e.g., optical motion capture or pose estimation from video). Finally, we identify key research challenges in gesture generation, including data availability and quality; producing human-like motion; grounding the gesture in the co-occurring speech in interaction with other speakers, and in the environment; performing gesture evaluation; and integration of gesture synthesis into applications. We highlight recent approaches to tackling the various key challenges, as well as the limitations of these approaches, and point toward areas of future development.
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    Project in Visualization and Data Analysis: Experiences in Designing and Coordinating the Course
    (The Eurographics Association, 2021) Kucher, Kostiantyn; Martins, Rafael M.; Kerren, Andreas; Sousa Santos, Beatriz and Domik, Gitta
    Visual analytics involves both visual and computational components for empowering human analysts who face the challenges of making sense and making use of large and heterogeneous data sets in various application domains. In order to facilitate the learning process for the students at higher education institutions with regard to both the theoretical knowledge and practical skills in visual analytics, the respective courses must cover a variety of topics and include multiple assessment methods and activities. In this paper, we report on the design and first instantiation of a full term project-based course in visualization and data analysis, which was recently offered to graduate and post-graduate students at our department and met with positive feedback from the course participants.
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    A Survey of Control Mechanisms for Creative Pattern Generation
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Gieseke, Lena; Asente, Paul; Mech, Radomir; Benes, Bedrich; Fuchs, Martin; Bühler, Katja and Rushmeier, Holly
    We review recent methods in 2D creative pattern generation and their control mechanisms, focusing on procedural methods. The review is motivated by an artist's perspective and investigates interactive pattern generation as a complex design problem. While the repetitive nature of patterns is well-suited to algorithmic creation and automation, an artist needs more flexible control mechanisms for adaptable and inventive designs. We organize the state of the art around pattern design features, such as repetition, frames, curves, directionality, and single visual accents. Within those areas, we summarize and discuss the techniques' control mechanisms for enabling artist intent. The discussion includes questions of how input is given by the artist, what type of content the artist inputs, where the input affects the canvas spatially, and when input can be given in the timeline of the creation process. We categorize the available control mechanisms on an algorithmic level and categorize their input modes based on exemplars, parameterization, handling, filling, guiding, and placing interactions. To better understand the potential of the current techniques for creative design and to make such an investigation more manageable, we motivate our discussion with how navigation, transparency, variation, and stimulation enable creativity. We conclude our review by identifying possible new directions that can inspire innovation for artist-centered creation processes and algorithms.
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    AvatarGo: Plug and Play self-avatars for VR
    (The Eurographics Association, 2022) Ponton, Jose Luis; Monclús, Eva; Pelechano, Nuria; Pelechano, Nuria; Vanderhaeghe, David
    The use of self-avatars in a VR application can enhance presence and embodiment which leads to a better user experience. In collaborative VR it also facilitates non-verbal communication. Currently it is possible to track a few body parts with cheap trackers and then apply IK methods to animate a character. However, the correspondence between trackers and avatar joints is typically fixed ad-hoc, which is enough to animate the avatar, but causes noticeable mismatches between the user's body pose and the avatar. In this paper we present a fast and easy to set up system to compute exact offset values, unique for each user, which leads to improvements in avatar movement. Our user study shows that the Sense of Embodiment increased significantly when using exact offsets as opposed to fixed ones. We also allowed the users to see a semitransparent avatar overlaid with their real body to objectively evaluate the quality of the avatar movement with our technique.
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    Radiance-Based Blender Add-On for Physically Accurate Rendering of Cultural Heritage
    (The Eurographics Association, 2023) Méndez, Míriam; Munoz-Pandiella, Imanol; Andujar, Carlos; Singh, Gurprit; Chu, Mengyu (Rachel)
    Despite the Cultural Heritage and Computer Graphics communities are increasingly joining forces to strengthen their collaboration, the study of how light interacts with monuments (e.g. weathering the surfaces or affecting the visitors' experience) is still an open problem in cultural heritage. A significant limitation is the lack of easy-to-use, open-source, physically-accurate tools allowing cultural heritage experts to perform lighting simulations on the increasing collection of 3D reconstructions. In this work, we present an open-source Blender add-on to facilitate such simulations. The add-on allows art historians to configure the properties (materials, lights, and camera) of the simulation, and uses as rendering back-end the Radiance software, a validated physically accurate light simulation tool. Our tool lowers the entry barrier for the use of a highly accurate but rather complex (command-based) tool for lighting studies in cultural heritage monuments.
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    Conveying Firsthand Experience: The Circuit Parcours Technique for Efficient and Engaging Teaching in Courses about Virtual Reality and Augmented Reality
    (The Eurographics Association, 2021) Dörner, Ralf; Horst, Robin; Sousa Santos, Beatriz and Domik, Gitta
    Providing the opportunity for hands-on experience is crucial when teaching courses about Virtual Reality (VR) and Augmented Reality (AR). However, the workload on the educator's side for providing these opportunities might be prohibitive. In addition, other organizational challenges can arise, for example, demonstrations of VR/AR application in a course might be too time-consuming, especially if the course is attended by many students. We present the Circuit Parcours Technique to meet these challenges. Here, in a well-organized event, stations with VR/AR demonstrations are provided in parallel, and students are enlisted to prepare and conduct the demonstrations. The event is embedded in a four-phase model. In this education paper, the technique is precisely described, examples for its flexible usage in different teaching situations are provided, advantages such as time efficiency are discussed, and lessons learned are shared from our experience with using this method for more than 10 years. Moreover, learning goals are identified that can be achieved with this technique besides gaining personal experience.
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    Visual Analysis of Point Cloud Neighborhoods via Multi-Scale Geometric Measures
    (The Eurographics Association, 2021) Ritter, Marcel; Schiffner, Daniel; Harders, Matthias; Theisel, Holger and Wimmer, Michael
    Point sets are a widely used spatial data structure in computational and observational domains, e.g. in physics particle simulations, computer graphics or remote sensing. Algorithms typically operate in local neighborhoods of point sets, for computing physical states, surface reconstructions, etc. We present a visualization technique based on multi-scale geometric features of such point clouds. We explore properties of different choices on the underlying weighted co-variance neighborhood descriptor, illustrated on different point set geometries and for varying noise levels. The impact of different weighting functions and tensor centroids, as well as point set features and noise levels becomes visible in the rotation-invariant feature images. We compare to a curvature based scale space visualization method and, finally, show how features in real-world LiDAR data can be inspected by images created with our approach in an interactive tool. In contrast to the curvature based approach, with our method line structures are highlighted over growing scales, with clear border regions to planar or spherical geometric structures.