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Now showing 1 - 9 of 9
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    Mesh Smoothing for Teaching GLSL Programming
    (The Eurographics Association, 2022) Ilinkin, Ivaylo; Bourdin, Jean-Jacques; Paquette, Eric
    This paper shares ideas for effective assignment that can be used to introduce a number of advanced GLSL concepts including shader storage buffer objects, transform feedback, and compute shaders. The assignment is based on published research on mesh smoothing which serves as a motivating factor and offers a sense of accomplishment.
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    State-of-the-Art in the Architecture, Methods and Applications of StyleGAN
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Bermano, Amit Haim; Gal, Rinon; Alaluf, Yuval; Mokady, Ron; Nitzan, Yotam; Tov, Omer; Patashnik, Or; Cohen-Or, Daniel; Meneveaux, Daniel; Patanè, Giuseppe
    Generative Adversarial Networks (GANs) have established themselves as a prevalent approach to image synthesis. Of these, StyleGAN offers a fascinating case study, owing to its remarkable visual quality and an ability to support a large array of downstream tasks. This state-of-the-art report covers the StyleGAN architecture, and the ways it has been employed since its conception, while also analyzing its severe limitations. It aims to be of use for both newcomers, who wish to get a grasp of the field, and for more experienced readers that might benefit from seeing current research trends and existing tools laid out. Among StyleGAN's most interesting aspects is its learned latent space. Despite being learned with no supervision, it is surprisingly well-behaved and remarkably disentangled. Combined with StyleGAN's visual quality, these properties gave rise to unparalleled editing capabilities. However, the control offered by StyleGAN is inherently limited to the generator's learned distribution, and can only be applied to images generated by StyleGAN itself. Seeking to bring StyleGAN's latent control to real-world scenarios, the study of GAN inversion and latent space embedding has quickly gained in popularity. Meanwhile, this same study has helped shed light on the inner workings and limitations of StyleGAN. We map out StyleGAN's impressive story through these investigations, and discuss the details that have made StyleGAN the go-to generator. We further elaborate on the visual priors StyleGAN constructs, and discuss their use in downstream discriminative tasks. Looking forward, we point out StyleGAN's limitations and speculate on current trends and promising directions for future research, such as task and target specific fine-tuning.
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    Scene Synthesis with Automated Generation of Textual Descriptions
    (The Eurographics Association, 2022) Müller-Huschke, Julian; Ritter, Marcel; Harders, Matthias; Pelechano, Nuria; Vanderhaeghe, David
    Most current research on automatically captioning and describing scenes with spatial content focuses on images. We outline that generating descriptive text for a synthesized 3D scene can be achieved via a suitable intermediate representation employed in the synthesis algorithm. As an example, we synthesize scenes of medieval village settings, and generate their descriptions. Our system employs graph grammars, Markov Chain Monte Carlo optimization, and a natural language generation pipeline. Randomly placed objects are evaluated and optimized by a cost function capturing neighborhood relations, path layouts, and collisions. Further, in a pilot study we assess the performance of our framework by comparing the generated descriptions to others provided by human subjects. While the latter were often short and low-effort, the highest-rated ones clearly outperform our generated ones. Nevertheless, the average of all collected human descriptions was indeed rated by the study participants as being less accurate than the automated ones.
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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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    NeuralMLS: Geometry-Aware Control Point Deformation
    (The Eurographics Association, 2022) Shechter, Meitar; Hanocka, Rana; Metzer, Gal; Giryes, Raja; Cohen-Or, Daniel; Pelechano, Nuria; Vanderhaeghe, David
    We introduce NeuralMLS, a space-based deformation technique, guided by a set of displaced control points. We leverage the power of neural networks to inject the underlying shape geometry into the deformation parameters. The goal of our technique is to enable a realistic and intuitive shape deformation. Our method is built upon moving least-squares (MLS), since it minimizes a weighted sum of the given control point displacements. Traditionally, the influence of each control point on every point in space (i.e., the weighting function) is defined using inverse distance heuristics. In this work, we opt to learn the weighting function, by training a neural network on the control points from a single input shape, and exploit the innate smoothness of neural networks. Our geometry-aware control point deformation is agnostic to the surface representation and quality; it can be applied to point clouds or meshes, including non-manifold and disconnected surface soups. We show that our technique facilitates intuitive piecewise smooth deformations, which are well suited for manufactured objects. We show the advantages of our approach compared to existing surface and space-based deformation techniques, both quantitatively and qualitatively.
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    Introduction to Computer Graphics: A Visual Interactive Approach
    (The Eurographics Association, 2022) Loscos, Celine; Bourdin, Jean-Jacques; Paquette, Eric
    Computer graphics is a difficult topic, requiring associating mathematics and programming skills. When initially taught at undergraduate levels, there are several factors which discourage students. First, programming a first computer graphics program requires a substantial initial framework which can be intimidating for many of them. Second, understanding and applying mathematical concepts is very often overwhelming. To counter this intimidating feeling, a new teaching approach was proposed in 2018 to 3rd year undergraduate computer science students. The course was split into two parts, theory and practice. The theoretical concepts were seen in class, with course handouts and table exercises resembling closely to traditional computer graphics learning. The originality of the course comes from a new way of practicing 3D programming. Practical labs were built upon the Unity game engine programming platform, adapted to match the theoretical concepts seen in classroom. Conclusions are drawn over 4 years of teaching this course. When taught using an accompanying easy-to-access graphics programming platform, computer graphics becomes a more attractive course for students with lower mathematics and programming skills. It is also very satisfactory for skillful students as it enables them to grab and master concepts quickly to reach interesting final lab achievements.
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    Safeguarding our Dance Cultural Heritage
    (The Eurographics Association, 2022) Aristidou, Andreas; Chalmers, Alan; Chrysanthou, Yiorgos; Loscos, Celine; Multon, Franck; Parkins, J. E.; Sarupuri, Bhuvan; Stavrakis, Efstathios; Hahmann, Stefanie; Patow, Gustavo A.
    Folk dancing is a key aspect of intangible cultural heritage that often reflects the socio-cultural and political influences prevailing in different periods and nations; each dance produces a meaning, a story with the help of music, costumes and dance moves. It has been transmitted from generation to generation, and to different countries, mainly due to movements of people carrying and disseminating their civilization. However, folk dancing, amongst other intangible heritage, is at high risk of disappearing due to wars, the moving of populations, economic crises, modernization, but most importantly, because these fragile creations have been modified over time through the process of collective recreation, and/or changes in the way of life. In this tutorial, we show how the European Project, SCHEDAR, exploited emerging technologies to digitize, analyze, and holistically document our intangible heritage creations, that is a critical necessity for the preservation and the continuity of our identity as Europeans.
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    RePiX VR - Learning environment for the Rendering Pipeline in Virtual Reality
    (The Eurographics Association, 2022) Heinemann, Birte; Görzen, Sergej; Schroeder, Ulrik; Bourdin, Jean-Jacques; Paquette, Eric
    Virtual reality can be used to support computer graphics teaching, e.g. by offering the chance to illustrate 3D processes that are difficult to convey. This paper describes the development and first evaluations of RePiX VR a virtual reality tool for computer graphics education, which focuses on the teaching of fundamental concepts of the rendering pipeline and offers researchers the opportunity to study learning in VR by integrating learning analytics. For this, the tool itself is presented and the evaluation, which uses quantitative methods and learning analytics to show the effectiveness of the tool. The first evaluations show that even learners without prior knowledge can use the VR tool and learn the first basics of computer graphics.
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    A Halfedge Refinement Rule for Parallel Loop Subdivision
    (The Eurographics Association, 2022) Vanhoey, Kenneth; Dupuy, Jonathan; Pelechano, Nuria; Vanderhaeghe, David
    We observe that a Loop refinement step invariably splits halfedges into four new ones. We leverage this observation to formulate a breadth-first uniform Loop subdivision algorithm: Our algorithm iterates over halfedges to both generate the refined topological information and scatter contributions to the refined vertex points. Thanks to this formulation we limit concurrent data access, enabling straightforward and efficient parallelization on the GPU. We provide an open-source GPU implementation that runs at state-of-the-art performances and supports production-ready assets, including borders and semi-sharp creases.