37-Issue 2

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https://diglib.eg.org/handle/10.2312/2632120

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EUROGRAPHICS 2018: CGF 37-2 Frontmatter
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Gutierrez, Diego; Sheffer, Alla; Gutierrez, Diego; Sheffer, Alla
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Sequences with Low-Discrepancy Blue-Noise 2-D Projections
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Perrier, Hélène; Coeurjolly, David; Xie, Feng; Pharr, Matt; Hanrahan, Pat; Ostromoukhov, Victor; Gutierrez, Diego and Sheffer, Alla
Distributions of samples play a very important role in rendering, affecting variance, bias and aliasing in Monte-Carlo and Quasi-Monte Carlo evaluation of the rendering equation. In this paper, we propose an original sampler which inherits many important features of classical low-discrepancy sequences (LDS): a high degree of uniformity of the achieved distribution of samples, computational efficiency and progressive sampling capability. At the same time, we purposely tailor our sampler in order to improve its spectral characteristics, which in turn play a crucial role in variance reduction, anti-aliasing and improving visual appearance of rendering. Our sampler can efficiently generate sequences of multidimensional points, whose power spectra approach so-called Blue-Noise (BN) spectral property while preserving low discrepancy (LD) in certain 2-D projections. In our tile-based approach, we perform permutations on subsets of the original Sobol LDS. In a large space of all possible permutations, we select those which better approach the target BN property, using pair-correlation statistics. We pre-calculate such ''good'' permutations for each possible Sobol pattern, and store them in a lookup table efficiently accessible in runtime. We provide a complete and rigorous proof that such permutations preserve dyadic partitioning and thus the LDS properties of the point set in 2-D projections. Our construction is computationally efficient, has a relatively low memory footprint and supports adaptive sampling. We validate our method by performing spectral/discrepancy/aliasing analysis of the achieved distributions, and provide variance analysis for several target integrands of theoretical and practical interest.
Semantic Segmentation for Line Drawing Vectorization Using Neural Networks
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Kim, Byungsoo; Wang, Oliver; Öztireli, A. Cengiz; Gross, Markus; Gutierrez, Diego and Sheffer, Alla
In this work, we present a method to vectorize raster images of line art. Inverting the rasterization procedure is inherently ill-conditioned, as there exist many possible vector images that could yield the same raster image. However, not all of these vector images are equally useful to the user, especially if performing further edits is desired. We therefore define the problem of computing an instance segmentation of the most likely set of paths that could have created the raster image. Once the segmentation is computed, we use existing vectorization approaches to vectorize each path, and then combine all paths into the final output vector image. To determine which set of paths is most likely, we train a pair of neural networks to provide semantic clues that help resolve ambiguities at intersection and overlap regions. These predictions are made considering the full context of the image, and are then globally combined by solving a Markov Random Field (MRF). We demonstrate the flexibility of our method by generating results on character datasets, a synthetic random line dataset, and a dataset composed of human drawn sketches. For all cases, our system accurately recovers paths that adhere to the semantics of the drawings.
ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Marnerides, Demetris; Bashford-Rogers, Thomas; Hatchett, Jon; Debattista, Kurt; Gutierrez, Diego and Sheffer, Alla
High dynamic range (HDR) imaging provides the capability of handling real world lighting as opposed to the traditional low dynamic range (LDR) which struggles to accurately represent images with higher dynamic range. However, most imaging content is still available only in LDR. This paper presents a method for generating HDR content from LDR content based on deep Convolutional Neural Networks (CNNs) termed ExpandNet. ExpandNet accepts LDR images as input and generates images with an expanded range in an end-to-end fashion. The model attempts to reconstruct missing information that was lost from the original signal due to quantization, clipping, tone mapping or gamma correction. The added information is reconstructed from learned features, as the network is trained in a supervised fashion using a dataset of HDR images. The approach is fully automatic and data driven; it does not require any heuristics or human expertise. ExpandNet uses a multiscale architecture which avoids the use of upsampling layers to improve image quality. The method performs well compared to expansion/inverse tone mapping operators quantitatively on multiple metrics, even for badly exposed inputs.
Approximate Program Smoothing Using Mean-Variance Statistics, with Application to Procedural Shader Bandlimiting
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Yang, Yuting; Barnes, Connelly; Gutierrez, Diego and Sheffer, Alla
We introduce a general method to approximate the convolution of a program with a Gaussian kernel. This results in the program being smoothed. Our compiler framework models intermediate values in the program as random variables, by using mean and variance statistics. We decompose the input program into atomic parts and relate the statistics of the different parts of the smoothed program. We give several approximate smoothing rules that can be used for the parts of the program. These include an improved variant of Dorn et al. [DBLW15], a novel adaptive Gaussian approximation, Monte Carlo sampling, and compactly supported kernels. Our adaptive Gaussian approximation handles multivariate Gaussian distributed inputs, gives exact results for a larger class of programs than previous work, and is accurate to the second order in the standard deviation of the kernel for programs with certain analytic properties. Because each expression in the program can have multiple approximation choices, we use a genetic search to automatically select the best approximations. We apply this framework to the problem of automatically bandlimiting procedural shader programs. We evaluate our method on a variety of geometries and complex shaders, including shaders with parallax mapping, animation, and spatially varying statistics. The resulting smoothed shader programs outperform previous approaches both numerically and aesthetically.
From Faces to Outdoor Light Probes
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Calian, Dan A.; Lalonde, Jean-François; Gotardo, Paulo; Simon, Tomas; Matthews, Iain; Mitchell, Kenny; Gutierrez, Diego and Sheffer, Alla
Image-based lighting has allowed the creation of photo-realistic computer-generated content. However, it requires the accurate capture of the illumination conditions, a task neither easy nor intuitive, especially to the average digital photography enthusiast. This paper presents an approach to directly estimate an HDR light probe from a single LDR photograph, shot outdoors with a consumer camera, without specialized calibration targets or equipment. Our insight is to use a person's face as an outdoor light probe. To estimate HDR light probes from LDR faces we use an inverse rendering approach which employs data-driven priors to guide the estimation of realistic, HDR lighting. We build compact, realistic representations of outdoor lighting both parametrically and in a data-driven way, by training a deep convolutional autoencoder on a large dataset of HDR sky environment maps. Our approach can recover high-frequency, extremely high dynamic range lighting environments. For quantitative evaluation of lighting estimation accuracy and relighting accuracy, we also contribute a new database of face photographs with corresponding HDR light probes. We show that relighting objects with HDR light probes estimated by our method yields realistic results in a wide variety of settings.
Fast Penetration Volume for Rigid Bodies
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Nirel, Dan; Lischinski, Dani; Gutierrez, Diego and Sheffer, Alla
Handling collisions among a large number of bodies can be a performance bottleneck in video games and many other real-time applications. We present a new framework for detecting and resolving collisions using the penetration volume as an interpenetration measure. Given two non-convex polyhedral bodies, a new sampling paradigm locates their near-contact configurations in advance, and stores associated contact information in a compact database. At runtime, we retrieve a given configuration's nearest neighbors. By taking advantage of the penetration volume's continuity, cheap geometric methods can use the neighbors to estimate contact information as well as a translational gradient. This results in an extremely fast, geometry-independent, and trivially parallelizable computation, which constitutes the first global volume-based collision resolution. When processing multiple collisions simultaneously on a 4-core processor, the average running cost is as low as 5 μs. Furthermore, no additional proximity or contact-regions queries are required. These results are orders of magnitude faster than previous penetration volume approaches.
A Physically Consistent Implicit Viscosity Solver for SPH Fluids
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Weiler, Marcel; Koschier, Dan; Brand, Magnus; Bender, Jan; Gutierrez, Diego and Sheffer, Alla
In this paper, we present a novel physically consistent implicit solver for the simulation of highly viscous fluids using the Smoothed Particle Hydrodynamics (SPH) formalism. Our method is the result of a theoretical and practical in-depth analysis of the most recent implicit SPH solvers for viscous materials. Based on our findings, we developed a list of requirements that are vital to produce a realistic motion of a viscous fluid. These essential requirements include momentum conservation, a physically meaningful behavior under temporal and spatial refinement, the absence of ghost forces induced by spurious viscosities and the ability to reproduce complex physical effects that can be observed in nature. On the basis of several theoretical analyses, quantitative academic comparisons and complex visual experiments we show that none of the recent approaches is able to satisfy all requirements. In contrast, our proposed method meets all demands and therefore produces realistic animations in highly complex scenarios. We demonstrate that our solver outperforms former approaches in terms of physical accuracy and memory consumption while it is comparable in terms of computational performance. In addition to the implicit viscosity solver, we present a method to simulate melting objects. Therefore, we generalize the viscosity model to a spatially varying viscosity field and provide an SPH discretization of the heat equation.
Terrain Super-resolution through Aerial Imagery and Fully Convolutional Networks
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Argudo, Oscar; Chica, Antonio; Andujar, Carlos; Gutierrez, Diego and Sheffer, Alla
Despite recent advances in surveying techniques, publicly available Digital Elevation Models (DEMs) of terrains are lowresolution except for selected places on Earth. In this paper we present a new method to turn low-resolution DEMs into plausible and faithful high-resolution terrains. Unlike other approaches for terrain synthesis/amplification (fractal noise, hydraulic and thermal erosion, multi-resolution dictionaries), we benefit from high-resolution aerial images to produce highly-detailed DEMs mimicking the features of the real terrain. We explore different architectures for Fully Convolutional Neural Networks to learn upsampling patterns for DEMs from detailed training sets (high-resolution DEMs and orthophotos), yielding up to one order of magnitude more resolution. Our comparative results show that our method outperforms competing data amplification approaches in terms of elevation accuracy and terrain plausibility.
Watercolor Woodblock Printing with Image Analysis
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Panotopoulou, Athina; Paris, Sylvain; Whiting, Emily; Gutierrez, Diego and Sheffer, Alla
Watercolor paintings have a unique look that mixes subtle color gradients and sophisticated diffusion patterns. This makes them immediately recognizable and gives them a unique appeal. Creating such paintings requires advanced skills that are beyond the reach of most people. Even for trained artists, producing several copies of a painting is a tedious task. One can resort to scanning an existing painting and printing replicas, but these are all identical and have lost an essential characteristic of a painting, its uniqueness. We address these two issues with a technique to fabricate woodblocks that we later use to create watercolor prints. The woodblocks can be reused to produce multiple copies but each print is unique due to the physical process that we introduce. We also design an image processing pipeline that helps users to create the woodblocks and describe a protocol that produces prints by carefully controlling the interplay between the paper, ink pigments, and water so that the final piece depicts the desired scene while exhibiting the distinctive features of watercolor. Our technique enables anyone with the resources to produce watercolor prints.
A New Microflake Model With Microscopic Self-shadowing for Accurate Volume Downsampling
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Loubet, Guillaume; Neyret, Fabrice; Gutierrez, Diego and Sheffer, Alla
Naive linear methods for downsampling high-resolution microflake volumes often produce inaccurate appearance, especially when input voxels are very opaque. Preserving correct appearance at all resolutions requires taking into account maskingshadowing effects that occur between and inside dense input voxels. We introduce a new microflake model whose additional parameters characterize self-shadowing effects at a microscopic scale. We provide an anisotropic self-shadowing function and microflake distributions for which the scattering coefficients and the phase functions of our model have closed-form expressions. We use this model in a new downsampling approach in which scattering parameters are computed from local estimations of self-shadowing probabilities in the input volume. Unlike previous work, our method handles datasets with spatially varying scattering parameters, semi-transparent volumes and datasets with intricate silhouettes. We show that our method generates LoDs with correct transparency and consistent appearance through scales for a wide range of challenging datasets, allowing for huge memory savings and efficient distant rendering without loss of quality.
Visual Attention for Rendered 3D Shapes
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Lavoué, Guillaume; Cordier, Frédéric; Seo, Hyewon; Larabi, Mohamed-Chaker; Gutierrez, Diego and Sheffer, Alla
Understanding the attentional behavior of the human visual system when visualizing a rendered 3D shape is of great importance for many computer graphics applications. Eye tracking remains the only solution to explore this complex cognitive mechanism. Unfortunately, despite the large number of studies dedicated to images and videos, only a few eye tracking experiments have been conducted using 3D shapes. Thus, potential factors that may influence the human gaze in the specific setting of 3D rendering, are still to be understood. In this work, we conduct two eye-tracking experiments involving 3D shapes, with both static and time-varying camera positions. We propose a method for mapping eye fixations (i.e., where humans gaze) onto the 3D shapes with the aim to produce a benchmark of 3D meshes with fixation density maps, which is publicly available. First, the collected data is used to study the influence of shape, camera position, material and illumination on visual attention. We find that material and lighting have a significant influence on attention, as well as the camera path in the case of dynamic scenes. Then, we compare the performance of four representative state-of-the-art mesh saliency models in predicting ground-truth fixations using two different metrics. We show that, even combined with a center-bias model, the performance of 3D saliency algorithms remains poor at predicting human fixations. To explain their weaknesses, we provide a qualitative analysis of the main factors that attract human attention. We finally provide a comparison of human-eye fixations and Schelling points and show that their correlation is weak.
Interactive Generation of Time-evolving, Snow-Covered Landscapes with Avalanches
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Cordonnier, Guillaume; Ecormier, Pierre; Galin, Eric; Gain, James; Benes, Bedrich; Cani, Marie-Paule; Gutierrez, Diego and Sheffer, Alla
We introduce a novel method for interactive generation of visually consistent, snow-covered landscapes and provide control of their dynamic evolution over time. Our main contribution is the real-time phenomenological simulation of avalanches and other user-guided events, such as tracks left by Nordic skiing, which can be applied to interactively sculpt the landscape. The terrain is modeled as a height field with additional layers for stable, compacted, unstable, and powdery snow, which behave in combination as a semi-viscous fluid. We incorporate the impact of several phenomena, including sunlight, temperature, prevailing wind direction, and skiing activities. The snow evolution includes snow-melt and snow-drift, which a ect stability of the snow mass and the probability of avalanches. A user can shape landscapes and their evolution either with a variety of interactive brushes, or by prescribing events along a winter season time-line. Our optimized GPU-implementation allows interactive updates of snow type and depth across a large (10 10km) terrain, including real-time avalanches, making this suitable for visual assets in computer games. We evaluate our method through perceptual comparison against exiting methods and real snow-depth data.
Feature Curve Co-Completion in Noisy Data
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Gehre, Anne; Lim, Isaak; Kobbelt, Leif; Gutierrez, Diego and Sheffer, Alla
Feature curves on 3D shapes provide important hints about significant parts of the geometry and reveal their underlying structure. However, when we process real world data, automatically detected feature curves are affected by measurement uncertainty, missing data, and sampling resolution, leading to noisy, fragmented, and incomplete feature curve networks. These artifacts make further processing unreliable. In this paper we analyze the global co-occurrence information in noisy feature curve networks to fill in missing data and suppress weakly supported feature curves. For this we propose an unsupervised approach to find meaningful structure within the incomplete data by detecting multiple occurrences of feature curve configurations (cooccurrence analysis). We cluster and merge these into feature curve templates, which we leverage to identify strongly supported feature curve segments as well as to complete missing data in the feature curve network. In the presence of significant noise, previous approaches had to resort to user input, while our method performs fully automatic feature curve co-completion. Finding feature reoccurrences however, is challenging since naïve feature curve comparison fails in this setting due to fragmentation and partial overlaps of curve segments. To tackle this problem we propose a robust method for partial curve matching. This provides us with the means to apply symmetry detection methods to identify co-occurring configurations. Finally, Bayesian model selection enables us to detect and group re-occurrences that describe the data well and with low redundancy.
GazeDirector: Fully Articulated Eye Gaze Redirection in Video
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Wood, Erroll; Baltrušaitis, Tadas; Morency, Louis-Philippe; Robinson, Peter; Bulling, Andreas; Gutierrez, Diego and Sheffer, Alla
We present GazeDirector, a new approach for eye gaze redirection that uses model-fitting. Our method first tracks the eyes by fitting a multi-part eye region model to video frames using analysis-by-synthesis, thereby recovering eye region shape, texture, pose, and gaze simultaneously. It then redirects gaze by 1) warping the eyelids from the original image using a model-derived flow field, and 2) rendering and compositing synthesized 3D eyeballs onto the output image in a photorealistic manner. GazeDirector allows us to change where people are looking without person-specific training data, and with full articulation, i.e. we can precisely specify new gaze directions in 3D. Quantitatively, we evaluate both model-fitting and gaze synthesis, with experiments for gaze estimation and redirection on the Columbia gaze dataset. Qualitatively, we compare GazeDirector against recent work on gaze redirection, showing better results especially for large redirection angles. Finally, we demonstrate gaze redirection on YouTube videos by introducing new 3D gaze targets and by manipulating visual behavior.
MIQP-based Layout Design for Building Interiors
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Wu, Wenming; Fan, Lubin; Liu, Ligang; Wonka, Peter; Gutierrez, Diego and Sheffer, Alla
We propose a hierarchical framework for the generation of building interiors. Our solution is based on a mixed integer quadratic programming (MIQP) formulation. We parametrize a layout by polygons that are further decomposed into small rectangles. We identify important high-level constraints, such as room size, room position, room adjacency, and the outline of the building, and formulate them in a way that is compatible with MIQP and the problem parametrization. We also propose a hierarchical framework to improve the scalability of the approach. We demonstrate that our algorithm can be used for residential building layouts and can be scaled up to large layouts such as office buildings, shopping malls, and supermarkets. We show that our method is faster by multiple orders of magnitude than previous methods.
Aura Mesh: Motion Retargeting to Preserve the Spatial Relationships between Skinned Characters
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Jin, Taeil; Kim, Meekyoung; Lee, Sung-Hee; Gutierrez, Diego and Sheffer, Alla
Applying motion-capture data to multi-person interaction between virtual characters is challenging because one needs to preserve the interaction semantics while also satisfying the general requirements of motion retargeting, such as preventing penetration and preserving naturalness. An efficient means of representing interaction semantics is by defining the spatial relationships between the body parts of characters. However, existing methods consider only the character skeleton and thus are not suitable for capturing skin-level spatial relationships. This paper proposes a novel method for retargeting interaction motions with respect to character skins. Specifically, we introduce the aura mesh, which is a volumetric mesh that surrounds a character's skin. The spatial relationships between two characters are computed from the overlap of the skin mesh of one character and the aura mesh of the other, and then the interaction motion retargeting is achieved by preserving the spatial relationships as much as possible while satisfying other constraints. We show the effectiveness of our method through a number of experiments.
A Versatile Parameterization for Measured Material Manifolds
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Soler, Cyril; Subr, Kartic; Nowrouzezahrai, Derek; Gutierrez, Diego and Sheffer, Alla
A popular approach for computing photorealistic images of virtual objects requires applying reflectance profiles measured from real surfaces, introducing several challenges: the memory needed to faithfully capture realistic material reflectance is large, the choice of materials is limited to the set of measurements, and image synthesis using the measured data is costly. Typically, this data is either compressed by projecting it onto a subset of its linear principal components or by applying non-linear methods. The former requires many components to faithfully represent the input reflectance, whereas the latter necessitates costly extrapolation algorithms. We learn an underlying, low-dimensional non-linear reflectance manifold amenable to rapid exploration and rendering of real-world materials. We can express interpolated materials as linear combinations of the measured data, despite them lying on an inherently non-linear manifold. This allows us to efficiently interpolate and extrapolate measured BRDFs, and to render directly from the manifold representation. We exploit properties of Gaussian process latent variable models and use our representation for high-performance and offline rendering with interpolated real-world materials.
Motion Sickness Simulation Based on Sensorimotor Control
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Hu, Chen-Hui; Lin, Wen-Chieh; Gutierrez, Diego and Sheffer, Alla
Sensorimotor control is an essential mechanism for human motions, from involuntary reflex actions to intentional motor skill learning, such as walking, jumping, and swimming. Humans perform various motions according to different task goals and physiological sensory perception; however, most existing computational approaches for motion simulation and generation rarely consider the effects of human perception. The assumption of perfect perception (i.e., no sensory errors) of existing approaches restricts the generated motion types and makes dynamical reactions less realistic. We propose a general framework for sensorimotor control, integrating a balance controller and a vestibular model, to generate perception-aware motions. By exploiting simulated perception, more natural responses that are closer to human reactions can be generated. For example, motion sickness caused by the impairments in the function of the vestibular system induces postural instability and body sway. Our approach generates physically correct motions and reasonable reactions to external stimuli since the spatial orientation estimation by the vestibular system is essential to preserve balance. We evaluate our framework by demonstrating standing balance on a rotational platform with different angular speeds and duration. The generated motions show that either faster angular speeds or longer rotational duration cause more severe motion sickness. Our results demonstrate that sensorimotor control, integrating human perception and physically-based control, offers considerable potential for providing more human-like behaviors, especially for perceptual illusions of human beings, including visual, proprioceptive, and tactile sensations.
Example-based Authoring of Procedural Modeling Programs with Structural and Continuous Variability
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Ritchie, Daniel; Jobalia, Sarah; Thomas, Anna; Gutierrez, Diego and Sheffer, Alla
Procedural models are a powerful tool for quickly creating a variety of computer graphics content. However, authoring them is challenging, requiring both programming and artistic expertise. In this paper, we present a method for learning procedural models from a small number of example objects. We focus on the modular design setting, where objects are constructed from a common library of parts. Our procedural representation is a probabilistic program that models both the discrete, hierarchical structure of the examples as well as the continuous variability in their spatial arrangements of parts. We develop an algorithm for learning such programs from examples, using combinatorial search over program structures and variational inference to estimate continuous program parameters. We evaluate our method by demonstrating its ability to learn programs from examples of ornamental designs, spaceships, space stations, and castles. Experiments suggest that our learned programs can reliably generate a variety of new objects that are perceptually indistinguishable from hand-crafted examples.

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