Search Results

Now showing 1 - 10 of 27
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    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, Francesco
    Convolutional 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.
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    Procedural 3D Asteroid Surface Detail Synthesis
    (The Eurographics Association, 2020) Li, Xi-zhi; Weller, René; Zachmann, Gabriel; Wilkie, Alexander and Banterle, Francesco
    We present a novel noise model to procedurally generate volumetric terrain on implicit surfaces. The main idea is to combine a novel Locally Controlled 3D Spot noise (LCSN) for authoring the macro structures and 3D Gabor noise to add micro details. More specifically, a spatially-defined kernel formulation in combination with an impulse distribution enables the LCSN to generate arbitrary size craters and boulders, while the Gabor noise generates stochastic Gaussian details. The corresponding metaball positions in the underlying implicit surface preserve locality to avoid the globality of traditional procedural noise textures, which yields an essential feature that is often missing in procedural texture based terrain generators. Furthermore, different noise-based primitives are integrated through operators, i.e. blending, replacing, or warping into the complex volumetric terrain. The result is a completely implicit representation and, as such, has the advantage of compactness as well as flexible user control. We applied our method to generating high quality asteroid meshes with fine surface details.
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    Neural Smoke Stylization with Color Transfer
    (The Eurographics Association, 2020) Christen, Fabienne; Kim, Byungsoo; Azevedo, Vinicius C.; Solenthaler, Barbara; Wilkie, Alexander and Banterle, Francesco
    Artistically controlling fluid simulations requires a large amount of manual work by an artist. The recently presented transportbased neural style transfer approach simplifies workflows as it transfers the style of arbitrary input images onto 3D smoke simulations. However, the method only modifies the shape of the fluid but omits color information. In this work, we therefore extend the previous approach to obtain a complete pipeline for transferring shape and color information onto 2D and 3D smoke simulations with neural networks. Our results demonstrate that our method successfully transfers colored style features consistently in space and time to smoke data for different input textures.
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    Fabric Appearance Benchmark
    (The Eurographics Association, 2020) Merzbach, Sebastian; Klein, Reinhard; Ritschel, Tobias and Eilertsen, Gabriel
    Appearance modeling is a difficult problem that still receives considerable attention from the graphics and vision communities. Though recent years have brought a growing number of high-quality material databases that have sparked new research, there is a general lack of evaluation benchmarks for performance assessment and fair comparisons between competing works. We therefore release a new dataset and pose a public challenge that will enable standardized evaluations. For this we measured 56 fabric samples with a commercial appearance scanner. We publish the resulting calibrated HDR images, along with baseline SVBRDF fits. The challenge is to recreate, under known light and view sampling, the appearance of a subset of unseen images. User submissions will be automatically evaluated and ranked by a set of standard image metrics.
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    A Survey on Sketch Based Content Creation: from the Desktop to Virtual and Augmented Reality
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Bhattacharjee, Sukanya; Chaudhuri, Parag; Mantiuk, Rafal and Sundstedt, Veronica
    Sketching is one of the most natural ways for representing any object pictorially. It is however, challenging to convert sketches to 3D content that is suitable for various applications like movies, games and computer aided design. With the advent of more accessible Virtual Reality (VR) and Augmented Reality (AR) technologies, sketching can potentially become a more powerful yet easy-to-use modality for content creation. In this state-of-the-art report, we aim to present a comprehensive overview of techniques related to sketch based content creation, both on the desktop and in VR/AR. We discuss various basic concepts related to static and dynamic content creation using sketches. We provide a structured review of various aspects of content creation including model generation, coloring and texturing, and finally animation. We try to motivate the advantages that VR/AR based sketching techniques and systems can offer into making sketch based content creation a more accessible and powerful mode of expression. We also discuss and highlight various unsolved challenges that current sketch based techniques face with the goal of encouraging future research in the domain.
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    Black Box Geometric Computing with Python: From Theory to Practice
    (The Eurographics Association, 2020) Koch, Sebastian; Schneider, Teseo; Li, Chengchen; Panozzo, Daniele; Fjeld, Morten and Frisvad, Jeppe Revall
    The first part of the course is theoretical, and introduces the finite element method trough interactive Jupyter notebooks. It also covers recent advancements toward an integrated pipeline, considering meshing and element design as a single challenge, leading to a black box pipeline that can solve simulations on ten thousand in the wild meshes, without any parameter tuning. In the second part we will move to practice, introducing a set of easy-to-use Python packages for applications in geometric computing. The presentation will have the form of live coding in a Jupyter notebook. We have designed the presented libraries to have a shallow learning curve, while also enabling programmers to easily accomplish a wide variety of complex tasks. Furthermore, these libraries utilize NumPy arrays as a common interface, making them highly composable with each-other as well as existing scientific computing packages. Finally, our libraries are blazing fast, doing most of the heavy computations in C++ with a minimal constant-overhead interface to Python. In the course, we will present a set of real-world examples from geometry processing, physical simulation, and geometric deep learning. Each example is prototypical of a common task in research or industry and is implemented in a few lines of code. By the end of the course, attendees will have exposure to a swiss-army-knife of simple, composable, and high-performance tools for geometric computing.
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    Conservative Ray Batching using Geometry Proxies
    (The Eurographics Association, 2020) Molenaar, Mathijs; Eisemann, Elmar; Wilkie, Alexander and Banterle, Francesco
    We present a method for improving batched ray traversal as was presented by Pharr et al. [PKGH97]. We propose to use conservative proxy geometry to more accurately determine whether a ray has a possibility of hitting any geometry that is stored on disk. This prevents unnecessary disk loads and thus reduces the disk bandwidth.
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    Integrating Local Collision Avoidance with Shortest Path Maps
    (The Eurographics Association, 2020) Sharma, Ritesh; Farias, Renato; Kallmann, Marcelo; Ritschel, Tobias and Eilertsen, Gabriel
    The effective integration of local collision avoidance with global path planning becomes a necessity when multi-agent systems need to be simulated in complex cluttered environments. This work presents our first results exploring the new approach of integrating Shortest Path Maps (SPMs) with local collision avoidance in order to provide optimal paths for agents to navigate around obstacles toward their goal locations. Our GPU-based SPM implementation is available.
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    Interactive Flat Coloring of Minimalist Neat Sketches
    (The Eurographics Association, 2020) Parakkat, Amal Dev; Madipally, Prudhviraj; Gowtham, Hari Hara; Cani, Marie-Paule; Wilkie, Alexander and Banterle, Francesco
    We introduce a simple Delaunay-triangulation based algorithm for the interactive coloring of neat line-art minimalist sketches, ie. vector sketches that may include open contours. The main objective is to minimize user intervention and make interaction as natural as with the flood-fill algorithm while extending coloring to regions with open contours. In particular, we want to save the user from worrying about parameters such as stroke weight and size. Our solution works in two steps, 1) a segmentation step in which the input sketch is automatically divided into regions based on the underlying Delaunay structure and 2) the interactive grouping of neighboring regions based on user input. More precisely, a region adjacency graph is computed from the segmentation result, and is interactively partitioned based on user input to generate the final colored sketch. Results show that our method is as natural as a bucket fill tool and powerful enough to color minimalist sketches.
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    Deep-Eyes: Fully Automatic Anime Character Colorization with Painting of Details on Empty Pupils
    (The Eurographics Association, 2020) Akita, Kenta; Morimoto, Yuki; Tsuruno, Reiji; Wilkie, Alexander and Banterle, Francesco
    Many studies have recently applied deep learning to the automatic colorization of line drawings. However, it is difficult to paint empty pupils using existing methods because the networks are trained with pupils that have edges, which are generated from color images using image processing. Most actual line drawings have empty pupils that artists must paint in. In this paper, we propose a novel network model that transfers the pupil details in a reference color image to input line drawings with empty pupils. We also propose a method for accurately and automatically coloring eyes. In this method, eye patches are extracted from a reference color image and automatically added to an input line drawing as color hints using our eye position estimation network.