37-Issue 2

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

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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.
PointProNets: Consolidation of Point Clouds with Convolutional Neural Networks
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Roveri, Riccardo; Öztireli, A. Cengiz; Pandele, Ioana; Gross, Markus; Gutierrez, Diego and Sheffer, Alla
With the widespread use of 3D acquisition devices, there is an increasing need of consolidating captured noisy and sparse point cloud data for accurate representation of the underlying structures. There are numerous algorithms that rely on a variety of assumptions such as local smoothness to tackle this ill-posed problem. However, such priors lead to loss of important features and geometric detail. Instead, we propose a novel data-driven approach for point cloud consolidation via a convolutional neural network based technique. Our method takes a sparse and noisy point cloud as input, and produces a dense point cloud accurately representing the underlying surface by resolving ambiguities in geometry. The resulting point set can then be used to reconstruct accurate manifold surfaces and estimate surface properties. To achieve this, we propose a generative neural network architecture that can input and output point clouds, unlocking a powerful set of tools from the deep learning literature. We use this architecture to apply convolutional neural networks to local patches of geometry for high quality and efficient point cloud consolidation. This results in significantly more accurate surfaces, as we illustrate with a diversity of examples and comparisons to the state-of-the-art.
Self-similarity Analysis for Motion Capture Cleaning
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Aristidou, Andreas; Cohen-Or, Daniel; Hodgins, Jessica K.; Shamir, Ariel; Gutierrez, Diego and Sheffer, Alla
Motion capture sequences may contain erroneous data, especially when the motion is complex or performers are interacting closely and occlusions are frequent. Common practice is to have specialists visually detect the abnormalities and fix them manually. In this paper, we present a method to automatically analyze and fix motion capture sequences by using self-similarity analysis. The premise of this work is that human motion data has a high-degree of self-similarity. Therefore, given enough motion data, erroneous motions are distinct when compared to other motions. We utilize motion-words that consist of short sequences of transformations of groups of joints around a given motion frame. We search for the K-nearest neighbors (KNN) set of each word using dynamic time warping and use it to detect and fix erroneous motions automatically. We demonstrate the effectiveness of our method in various examples, and evaluate by comparing to alternative methods and to manual cleaning.
PCPNet: Learning Local Shape Properties from Raw Point Clouds
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Guerrero, Paul; Kleiman, Yanir; Ovsjanikov, Maks; Mitra, Niloy J.; Gutierrez, Diego and Sheffer, Alla
In this paper, we propose PCPNET, a deep-learning based approach for estimating local 3D shape properties in point clouds. In contrast to the majority of prior techniques that concentrate on global or mid-level attributes, e.g., for shape classification or semantic labeling, we suggest a patch-based learning method, in which a series of local patches at multiple scales around each point is encoded in a structured manner. Our approach is especially well-adapted for estimating local shape properties such as normals (both unoriented and oriented) and curvature from raw point clouds in the presence of strong noise and multi-scale features. Our main contributions include both a novel multi-scale variant of the recently proposed PointNet architecture with emphasis on local shape information, and a series of novel applications in which we demonstrate how learning from training data arising from well-structured triangle meshes, and applying the trained model to noisy point clouds can produce superior results compared to specialized state-of-the-art techniques. Finally, we demonstrate the utility of our approach in the context of shape reconstruction, by showing how it can be used to extract normal orientation information from point clouds.
Real-time Locomotion Controller using an Inverted-Pendulum-based Abstract Model
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Hwang, Jaepyung; Kim, Jongmin; Suh, Il Hong; Kwon, Taesoo; Gutierrez, Diego and Sheffer, Alla
In this paper, we propose a novel motion controller for the online generation of natural character locomotion that adapts to new situations such as changing user control or applying external forces. This controller continuously estimates the next footstep while walking and running, and automatically switches the stepping strategy based on situational changes. To develop the controller, we devise a new physical model called an inverted-pendulum-based abstract model (IPAM). The proposed abstract model represents high-dimensional character motions, inheriting the naturalness of captured motions by estimating the appropriate footstep location, speed and switching time at every frame. The estimation is achieved by a deep learning based regressor that extracts important features in captured motions. To validate the proposed controller, we train the model using captured motions of a human stopping, walking, and running in a limited space. Then, the motion controller generates humanlike locomotion with continuously varying speeds, transitions between walking and running, and collision response strategies in a cluttered space in real time.
Fast Catmull-Rom Spline Interpolation for High-Quality Texture Sampling
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Csébfalvi, Balázs; Gutierrez, Diego and Sheffer, Alla
It is well known that cubic texture filtering can be efficiently implemented on the GPU by using a method published by Sigg and Hadwiger [SH05], which simplifies the evaluation to a linear combination of linear texture fetches. However, their method cannot be directly applied if the filter kernel takes also negative values like the popular Catmull-Rom spline, for example. In this paper, we propose a modified algorithm that is able to handle also the negative weights. Therefore, using our method, the Catmull-Rom spline interpolation can also be evaluated in one, two, and three dimensions by taking two, four, and eight linear texture fetches, respectively.
Single-image Tomography: 3D Volumes from 2D Cranial X-Rays
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Henzler, Philipp; Rasche, Volker; Ropinski, Timo; Ritschel, Tobias; Gutierrez, Diego and Sheffer, Alla
As many different 3D volumes could produce the same 2D x-ray image, inverting this process is challenging. We show that recent deep learning-based convolutional neural networks can solve this task. As the main challenge in learning is the sheer amount of data created when extending the 2D image into a 3D volume, we suggest firstly to learn a coarse, fixed-resolution volume which is then fused in a second step with the input x-ray into a high-resolution volume. To train and validate our approach we introduce a new dataset that comprises of close to half a million computer-simulated 2D x-ray images of 3D volumes scanned from 175 mammalian species. Future applications of our approach include stereoscopic rendering of legacy x-ray images, re-rendering of x-rays including changes of illumination, view pose or geometry. Our evaluation includes comparison to previous tomography work, previous learning methods using our data, a user study and application to a set of real x-rays.
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.
String Art: Towards Computational Fabrication of String Images
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Birsak, Michael; Rist, Florian; Wonka, Peter; Musialski, Przemyslaw; Gutierrez, Diego and Sheffer, Alla
In this paper we propose a novel method for the automatic computation and digital fabrication of artistic string images. String art is a technique used by artists for the creation of abstracted images which are composed of straight lines of strings tensioned between pins distributed on a frame. Together the strings fuse to a perceptible image. Traditionally, artists craft such images manually in a highly sophisticated and tedious design process. To achieve this goal fully automatically we propose a computational setup driven by a discrete optimization algorithm which takes an ordinary picture as input and converts it into a connected graph of strings that tries to reassemble the input image best possibly. Furthermore, we propose a hardware setup for automatic digital fabrication of these images using an industrial robot that spans the strings. Finally, we demonstrate the applicability of our approach by generating and fabricating a set of real string art images.
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.
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.
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.
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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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.
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.
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.

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