EG 2020 - Short Papers

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https://diglib.eg.org/handle/10.2312/2632887

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Accelerated Foveated Rendering based on Adaptive Tessellation
(The Eurographics Association, 2020) Tiwary, Ankur; Ramanathan, Muthuganapathy; Kosinka, Jiri; Wilkie, Alexander and Banterle, Francesco
We propose an optimization method for adaptive geometric tessellation, involving the saccadic motion of the human eye and foveated rendering. Increased demands on computational resources, especially in the field of head-mounted devices with gaze contingency make optimization schemes pertinent for a seamless user experience. For implementing foveated rendering, our algorithm tessellates a 3D model in real-time based on the location of the user's gaze, substituted with a mouse cursor in this project as a proof of concept. Saccades and fixations of the human eye are simulated by delaying the process of tessellation and rendering by the minimum time taken to complete a saccade. Calculations required for tessellation and rendering the changes on the screen are stalled as and when the eye fixates after a saccade. The paper walks through our contribution by describing the theory, the application method, and results from our user study evaluating our method.
Adversarial Generation of Continuous Implicit Shape Representations
(The Eurographics Association, 2020) Kleineberg, Marian; Fey, Matthias; Weichert, Frank; Wilkie, Alexander and Banterle, Francesco
This work presents a generative adversarial architecture for generating three-dimensional shapes based on signed distance representations. While the deep generation of shapes has been mostly tackled by voxel and surface point cloud approaches, our generator learns to approximate the signed distance for any point in space given prior latent information. Although structurally similar to generative point cloud approaches, this formulation can be evaluated with arbitrary point density during inference, leading to fine-grained details in generated outputs. Furthermore, we study the effects of using either progressively growing voxel- or point-processing networks as discriminators, and propose a refinement scheme to strengthen the generator's capabilities in modeling the zero iso-surface decision boundary of shapes. We train our approach on the SHAPENET benchmark dataset and validate, both quantitatively and qualitatively, its performance in generating realistic 3D shapes.
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.
Controllable Caustic Animation Using Vector Fields
(The Eurographics Association, 2020) Rojo, Irene Baeza; Gross, Markus; Günther, Tobias; Wilkie, Alexander and Banterle, Francesco
In movie production, lighting is commonly used to redirect attention or to set the mood in a scene. The detailed editing of complex lighting phenomena, however, is as tedious as it is important, especially with dynamic lights or when light is a relevant story element. In this paper, we propose a new method to create caustic animations, which are controllable through constraints drawn by the user. Our method blends caustics into a specified target image by treating photons as particles that move in a divergence-free fluid, an irrotational vector field or a linear combination of the two. Once described as a flow, additional user constraints are easily added, e.g., to direct the flow, create boundaries or add synthetic turbulence, which offers new ways to redirect and control light. The corresponding vector field is computed by fitting a stream function and a scalar potential per time step, for which constraints are described in a quadratic energy that we minimize as a linear least squares problem. Finally, photons are placed at their new positions back into the scene and are rendered with progressive photon mapping.
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.
First Order Signed Distance Fields
(The Eurographics Association, 2020) Bán, Róbert; Valasek, Gábor; Wilkie, Alexander and Banterle, Francesco
This paper investigates a first order generalization of signed distance fields. We show that we can improve accuracy and storage efficiency by incorporating the spatial derivatives of the signed distance function into the distance field samples. We show that a representation in power basis remains invariant under barycentric combination, as such, it is interpolated exactly by the GPU. Our construction is applicable in any geometric setting where point-surface distances can be queried. To emphasize the practical advantages of this approach, we apply our results to signed distance field generation from triangular meshes. We propose storage optimization approaches and offer a theoretical and empirical accuracy analysis of our proposed distance field type in relation to traditional, zero order distance fields. We show that the proposed representation may offer an order of magnitude improvement in storage while retaining the same precision as a higher resolution distance field.
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.
Interactive Assembly and Animation of 3D Digital Garments
(The Eurographics Association, 2020) Nylén, Oskar; Pall, Pontus; Ishiwaka, Yuko; Suda, Kazuto; Fratarcangeli, Marco; Wilkie, Alexander and Banterle, Francesco
We present a novel real-time tool for sewing together 2D patterns, enabling quick assembly of visually plausible, interactively animated garments for virtual characters. The process is assisted by ad-hoc visual hints and allows designers to import 2D patterns from any CAD-tool, connect them using seams around a 3D character with any body type, and assess the overall quality during the character animation. The cloth is numerically simulated including robust modeling of contact of the cloth with itself and with the character's body. Overall, our tool allows for fast prototyping of virtual garments, achieving immediate feedback on their behaviour and visual quality on an animated character, in effect speeding up the content production pipeline for visual effects applications involving clothed characters.
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.
MEPP2: A Generic Platform for Processing 3D Meshes and Point Clouds
(The Eurographics Association, 2020) Vidal, Vincent; Lombardi, Eric; Tola, Martial; Dupont, Florent; Lavoué, Guillaume; Wilkie, Alexander and Banterle, Francesco
In this paper, we present MEPP2, an open-source C++ software development kit (SDK) for processing and visualizing 3D surface meshes and point clouds. It provides both an application programming interface (API) for creating new processing filters and a graphical user interface (GUI) that facilitates the integration of new filters as plugins. Static and dynamic 3D meshes and point clouds with appearance-related attributes (color, texture information, normal) are supported. The strength of the platform is to be generic programming oriented. It offers an abstraction layer, based on C++ Concepts, that provides interoperability over several third party mesh and point cloud data structures, such as OpenMesh, CGAL, and PCL. Generic code can be run on all data structures implementing the required concepts, which allows for performance and memory footprint comparison. Our platform also permits to create complex processing pipelines gathering idiosyncratic functionalities of the different libraries. We provide examples of such applications. MEPP2 runs on Windows, Linux & Mac OS X and is intended for engineers, researchers, but also students thanks to simple use, facilitated by the proposed architecture and extensive documentation.
Multisample Anti-aliasing in Deferred Rendering
(The Eurographics Association, 2020) Fridvalszky, András; Tóth, Balázs; Wilkie, Alexander and Banterle, Francesco
We propose a novel method for multisample anti-aliasing in deferred shading. Our technique successfully reduces memory and bandwidth usage. The new model uses per-pixel linked lists to store the samples. We also introduce algorithms to construct the new G-Buffer in the geometry pass and to calculate the shading in the lighting pass. The algorithms are designed to enable further optimizations, similar to variable rate shading. We also propose methods to satisfy constraints of memory usage and processing time. We integrated the new method into a Vulkan based renderer.
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.
Photon Mapping Superluminal Particles
(The Eurographics Association, 2020) Waldemarson, Gustaf; Doggett, Michael; Wilkie, Alexander and Banterle, Francesco
One type of light source that remains largely unexplored in the field of light transport rendering is the light generated by superluminal particles, a phenomenon more commonly known as Cherenkov radiation [Cˇ37]. By re-purposing the Frank-Tamm equation [FT91] for rendering, the energy output of these particles can be estimated and consequently mapped to photons, making it possible to visualize the brilliant blue light characteristic of the effect. In this paper we extend a stochastic progressive photon mapper and simulate the emission of superluminal particles from a source object close to a medium with a high index of refraction. In practice, the source is treated as a new kind of light source, allowing us to efficiently reuse existing photon mapping methods.
A Practical Male Hair Aging Model
(The Eurographics Association, 2020) Volkmann, Diego V.; Walter, Marcelo; Wilkie, Alexander and Banterle, Francesco
The modeling and rendering of hair in Computer Graphics have seen much progress in the last few years. However, modeling and rendering hair aging, visually seen as the loss of pigments, have not attracted the same attention. We introduce in this paper a biologically inspired hair aging system with two main parts: greying of individual hairs, and time evolution of greying over the scalp. The greying of individual hairs is based on current knowledge about melanin loss, whereas the evolution on the scalp is modeled by segmenting the scalp in regions and defining distinct time frames for greying to occur. Our experimental visual results present plausible results despite the relatively simple model. We validate the results by presenting side by side our results with real pictures of men at different ages.
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.
UV Completion with Self-referenced Discrimination
(The Eurographics Association, 2020) Kang, Jiwoo; Lee, Seongmin; Lee, Sanghoon; Wilkie, Alexander and Banterle, Francesco
A facial UV map is used in many applications such as facial reconstruction, synthesis, recognition, and editing. However, it is difficult to collect a number of the UVs needed for accuracy using 3D scan device, or a multi-view capturing system should be required to construct the UV. An occluded facial UV with holes could be obtained by sampling an image after fitting a 3D facial model by recent alignment methods. In this paper, we introduce a facial UV completion framework to train the deep neural network with a set of incomplete UV textures. By using the fact that the facial texture distributions of the left and right half-sides are almost equal, we devise an adversarial network to model the complete UV distribution of the facial texture. Also, we propose the self-referenced discrimination scheme that uses the facial UV completed from the generator for training real distribution. It is demonstrated that the network can be trained to complete the facial texture with incomplete UVs comparably to when utilizing the ground-truth UVs.

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Browsing EG 2020 - Short Papers by Subject "Computing methodologies"

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