100 results
Search Results
Now showing 1 - 10 of 100
Item EUROGRAPHICS 2020: Short Papers Frontmatter(Eurographics Association, 2020) Wilkie, Alexander; Banterle, Francesco; Wilkie, Alexander and Banterle, FrancescoItem State of the Art on Computational Design of Assemblies with Rigid Parts(The Eurographics Association and John Wiley & Sons Ltd., 2021) Wang, Ziqi; Song, Peng; Pauly, Mark; Bühler, Katja and Rushmeier, HollyAn assembly refers to a collection of parts joined together to achieve a specific form and/or functionality. Designing assemblies is a non-trivial task as a slight local modification on a part's geometry or its joining method could have a global impact on the structural and/or functional performance of the whole assembly. Assemblies can be classified as structures that transmit force to carry loads and mechanisms that transfer motion and force to perform mechanical work. In this state-of-the-art report, we focus on computational design of structures with rigid parts, which generally can be formulated as a geometric modeling and optimization problem. We broadly classify existing computational design approaches, mainly from the computer graphics community, according to high-level design objectives, including fabricability, structural stability, reconfigurability, and tileability. Computational analysis of various aspects of assemblies is an integral component in these design approaches. We review different classes of computational analysis and design methods, discuss their strengths and limitations, make connections among them, and propose possible directions for future research.Item EUROGRAPHICS 2020: CGF 39-2 STARs Frontmatter(The Eurographics Association and John Wiley & Sons Ltd., 2020) Mantiuk, Rafal; Sundstedt, Veronica; Mantiuk, Rafal and Sundstedt, Veronica-Item Triplanar Displacement Mapping for Terrain Rendering(The Eurographics Association, 2020) Weiss, Sebastian; Bayer, Florian; Westermann, Rüdiger; Wilkie, Alexander and Banterle, FrancescoHeightmap-based terrain representations are common in computer games and simulations. However, adding geometric details to such a representation during rendering on the GPU is difficult to achieve. In this paper, we propose a combination of triplanar mapping, displacement mapping, and tessellation on the GPU, to create extruded geometry along steep faces of heightmap-based terrain fields on-the-fky during rendering. The method allows rendering geometric details such as overhangs and boulders, without explicit triangulation. We further demonstrate how to handle collisions and shadows for the enriched geometry.Item Unsupervised Learning of Disentangled 3D Representation from a Single Image(The Eurographics Association, 2021) Lv, Junliang; Jiang, Haiyong; Xiao, Jun; Bittner, Jirà and Waldner, ManuelaLearning 3D representation of a single image is challenging considering the ambiguity, occlusion, and perspective project of an object in an image. Previous works either seek image annotation or 3D supervision to learn meaningful factors of an object or employ a StyleGAN-like framework for image synthesis. While the first ones rely on tedious annotation and even dense geometry ground truth, the second solutions usually cannot guarantee consistency of shapes between different view images. In this paper, we combine the advantages of both frameworks and propose an image disentanglement method based on 3D representation. Results show our method facilitates unsupervised 3D representation learning while preserving consistency between images.Item Recreating Past and Present: An Exceptional Student-Created Virtual Heritage Experience(The Eurographics Association, 2020) Anderson, Eike Falk; Sloan, Susan; Romero, Mario and Sousa Santos, BeatriceWe present an outstanding undergraduate student project in the form of a virtual heritage experience, created by a multidisciplinary group of six 4th semester undergraduate students from a range of computer graphics related programmes of study, ranging from 3D art and design to graphics software development. The "Exercise Smash" application allows participants to take part in a 1944 military exercise that was held in preparation of the D-Day landings in Normandy, during which several amphibious tanks sank, and then to dive down to the tank wrecks on the modern-day seafloor. The virtual heritage experience was presented during a public event at a military history museum and has also been demonstrated at an archaeology conference, being well-received in both cases.Item Generative Landmarks(The Eurographics Association, 2021) Ferman, David; Bharaj, Gaurav; Bittner, Jirà and Waldner, ManuelaWe propose a general purpose approach to detect landmarks with improved temporal consistency, and personalization. Most sparse landmark detection methods rely on laborious, manually labelled landmarks, where inconsistency in annotations over a temporal volume leads to sub-optimal landmark learning. Further, high-quality landmarks with personalization is often hard to achieve. We pose landmark detection as an image translation problem. We capture two sets of unpaired marked (with paint) and unmarked videos. We then use a generative adversarial network and cyclic consistency to predict deformations of landmark templates that simulate markers on unmarked images until these images are indistinguishable from ground-truth marked images. Our novel method does not rely on manually labelled priors, is temporally consistent, and image class agnostic - face, and hand landmarks detection examples are shown.Item 2D Points Curve Reconstruction Survey and Benchmark(The Eurographics Association and John Wiley & Sons Ltd., 2021) Ohrhallinger, Stefan; Peethambaran, Jiju; Parakkat, Amal Dev; Dey, Tamal Krishna; Muthuganapathy, Ramanathan; Bühler, Katja and Rushmeier, HollyCurve reconstruction from unstructured points in a plane is a fundamental problem with many applications that has generated research interest for decades. Involved aspects like handling open, sharp, multiple and non-manifold outlines, run-time and provability as well as potential extension to 3D for surface reconstruction have led to many different algorithms. We survey the literature on 2D curve reconstruction and then present an open-sourced benchmark for the experimental study. Our unprecedented evaluation of a selected set of planar curve reconstruction algorithms aims to give an overview of both quantitative analysis and qualitative aspects for helping users to select the right algorithm for specific problems in the field. Our benchmark framework is available online to permit reproducing the results and easy integration of new algorithms.Item State of the Art on Neural Rendering(The Eurographics Association and John Wiley & Sons Ltd., 2020) Tewari, Ayush; Fried, Ohad; Thies, Justus; Sitzmann, Vincent; Lombardi, Stephen; Sunkavalli, Kalyan; Martin-Brualla, Ricardo; Simon, Tomas; Saragih, Jason; Nießner, Matthias; Pandey, Rohit; Fanello, Sean; Wetzstein, Gordon; Zhu, Jun-Yan; Theobalt, Christian; Agrawala, Maneesh; Shechtman, Eli; Goldman, Dan B.; Zollhöfer, Michael; Mantiuk, Rafal and Sundstedt, VeronicaEfficient rendering of photo-realistic virtual worlds is a long standing effort of computer graphics. Modern graphics techniques have succeeded in synthesizing photo-realistic images from hand-crafted scene representations. However, the automatic generation of shape, materials, lighting, and other aspects of scenes remains a challenging problem that, if solved, would make photo-realistic computer graphics more widely accessible. Concurrently, progress in computer vision and machine learning have given rise to a new approach to image synthesis and editing, namely deep generative models. Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e.g., by the integration of differentiable rendering into network training. With a plethora of applications in computer graphics and vision, neural rendering is poised to become a new area in the graphics community, yet no survey of this emerging field exists. This state-of-the-art report summarizes the recent trends and applications of neural rendering. We focus on approaches that combine classic computer graphics techniques with deep generative models to obtain controllable and photorealistic outputs. Starting with an overview of the underlying computer graphics and machine learning concepts, we discuss critical aspects of neural rendering approaches. Specifically, our emphasis is on the type of control, i.e., how the control is provided, which parts of the pipeline are learned, explicit vs. implicit control, generalization, and stochastic vs. deterministic synthesis. The second half of this state-of-the-art report is focused on the many important use cases for the described algorithms such as novel view synthesis, semantic photo manipulation, facial and body reenactment, relighting, free-viewpoint video, and the creation of photo-realistic avatars for virtual and augmented reality telepresence. Finally, we conclude with a discussion of the social implications of such technology and investigate open research problems.Item Frequency-Aware Reconstruction of Fluid Simulations with Generative Networks(The Eurographics Association, 2020) Biland, Simon; Azevedo, Vinicius C.; Kim, Byungsoo; Solenthaler, Barbara; Wilkie, Alexander and Banterle, FrancescoConvolutional neural networks were recently employed to fully reconstruct fluid simulation data from a set of reduced parameters. However, since (de-)convolutions traditionally trained with supervised l1-loss functions do not discriminate between low and high frequencies in the data, the error is not minimized efficiently for higher bands. This directly correlates with the quality of the perceived results, since missing high frequency details are easily noticeable. In this paper, we analyze the reconstruction quality of generative networks and present a frequency-aware loss function that is able to focus on specific bands of the dataset during training time. We show that our approach improves reconstruction quality of fluid simulation data in mid-frequency bands, yielding perceptually better results while requiring comparable training time.