37-Issue 2

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

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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.
Packable Springs
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Wolff, Katja; Poranne, Roi; Glauser, Oliver; Sorkine-Hornung, Olga; Gutierrez, Diego and Sheffer, Alla
Laser cutting is an appealing fabrication process due to the low cost of materials and extremely fast fabrication. However, the design space afforded by laser cutting is limited, since only flat panels can be cut. Previous methods for manufacturing from flat sheets usually roughly approximate 3D objects by polyhedrons or cross sections. Computational design methods for connecting, interlocking, or folding several laser cut panels have been introduced; to obtain a good approximation, these methods require numerous parts and long assembly times. In this paper, we propose a radically different approach: Our approximation is based on cutting thin, planar spirals out of flat panels. When such spirals are pulled apart, they take on the shape of a 3D spring whose contours are similar to the input object. We devise an optimization problem that aims to minimize the number of required parts, thus reducing costs and fabrication time, while at the same time ensuring that the resulting spring mimics the shape of the original object. In addition to rapid fabrication and assembly, our method enables compact packaging and storage as flat parts. We also demonstrate its use for creating armatures for sculptures and moulds for filling, with potential applications in architecture or construction.
Hair Modeling and Simulation by Style
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Jung, Seunghwan; Lee, Sung-Hee; Gutierrez, Diego and Sheffer, Alla
As the deformation behaviors of hair strands vary greatly depending on the hairstyle, the computational cost and accuracy of hair movement simulations can be significantly improved by applying simulation methods specific to a certain style. This paper makes two contributions with regard to the simulation of various hair styles. First, we propose a novel method to reconstruct simulatable hair strands from hair meshes created by artists. Manually created hair meshes consist of numerous mesh patches, and the strand reconstruction process is challenged by the absence of connectivity information among the patches for the same strand and the omission of hidden parts of strands due to the manual creation process. To this end, we develop a two-stage spectral clustering method for estimating the degree of connectivity among patches and a strand-growing method that preserves hairstyles. Next, we develop a hairstyle classification method for style-specific simulations. In particular, we propose a set of features for efficient classifications and show that classifiers trained with the proposed features have higher accuracy than those trained with naive features. Our method applies efficient simulation methods according to the hairstyle without specific user input, and thus is favorable for real-time simulation.
Efficient BVH-based Collision Detection Scheme with Ordering and Restructuring
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Wang, Xinlei; Tang, Min; Manocha, Dinesh; Tong, Ruofeng; Gutierrez, Diego and Sheffer, Alla
Bounding volume hierarchy (BVH) has been widely adopted as the acceleration structure in broad-phase collision detection. Previous state-of-the-art BVH-based collision detection approaches exploited the spatio-temporal coherence of simulations by maintaining a bounding volume test tree (BVTT) front. A major drawback of these algorithms is that large deformations in the scenes decrease culling efficiency and slow down collision queries. Moreover, for front-based methods, the inefficient caching on GPU caused by the arbitrary layout of BVH and BVTT front nodes becomes a critical performance issue. We present a fast and robust BVH-based collision detection scheme on GPU that addresses the above problems by ordering and restructuring BVHs and BVTT fronts. Our techniques are based on the use of histogram sort and an auxiliary structure BVTT front log, through which we analyze the dynamic status of BVTT front and BVH quality. Our approach efficiently handles inter- and intra-object collisions and performs especially well in simulations where there is considerable spatio-temporal coherence. The benchmark results demonstrate that our approach is significantly faster than the previous BVH-based method, and also outperforms other state-of-the-art spatial subdivision schemes in terms of speed.
Wavejets: A Local Frequency Framework for Shape Details Amplification
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Béarzi, Yohann; Digne, Julie; Chaine, Raphaëlle; Gutierrez, Diego and Sheffer, Alla
Detail enhancement is a well-studied area of 3D rendering and image processing, which has few equivalents for 3D shape processing. To enhance details, one needs an efficient analysis tool to express the local surface dynamics.We introduceWavejets, a new function basis for locally decomposing a shape expressed over the local tangent plane, by considering both angular oscillations of the surface around each point and a radial polynomial.We link theWavejets coefficients to surface derivatives and give theoretical guarantees for their precision and stability with respect to an approximate tangent plane. The coefficients can be used for shape details amplification, to enhance, invert or distort them, by operating either on the surface point positions or on the normals. From a practical point of view, we derive an efficient way of estimating Wavejets on point sets and demonstrate experimentally the amplification results with respect to noise or basis truncation.
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.
Deep Joint Design of Color Filter Arrays and Demosaicing
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Henz, Bernardo; Gastal, Eduardo S. L.; Oliveira, Manuel M.; Gutierrez, Diego and Sheffer, Alla
We present a convolutional neural network architecture for performing joint design of color filter array (CFA) patterns and demosaicing. Our generic model allows the training of CFAs of arbitrary sizes, optimizing each color filter over the entire RGB color space. The patterns and algorithms produced by our method provide high-quality color reconstructions. We demonstrate the effectiveness of our approach by showing that its results achieve higher PSNR than the ones obtained with state-of-the-art techniques on all standard demosaicing datasets, both for noise-free and noisy scenarios. Our method can also be used to obtain demosaicing strategies for pre-defined CFAs, such as the Bayer pattern, for which our results also surpass even the demosaicing algorithms specifically designed for such a pattern.
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.
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.
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.
Repairing Inconsistent Curve Networks on Non-parallel Cross-sections
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Huang, Zhi Yang; Holloway, Michelle; Carr, Nathan; Ju, Tao; Gutierrez, Diego and Sheffer, Alla
In this work we present the first algorithm for restoring consistency between curve networks on non-parallel cross-sections. Our method addresses a critical but overlooked challenge in the reconstruction process from cross-sections that stems from the fact that cross-sectional slices are often generated independently of one another, such as in interactive volume segmentation. As a result, the curve networks on two non-parallel slices may disagree where the slices intersect, which makes these crosssections an invalid input for surfacing. We propose a method that takes as input an arbitrary number of non-parallel slices, each partitioned into two or more labels by a curve network, and outputs a modified set of curve networks on these slices that are guaranteed to be consistent. We formulate the task of restoring consistency while preserving the shape of input curves as a constrained optimization problem, and we propose an effective solution framework. We demonstrate our method on a data-set of complex multi-labeled input cross-sections. Our technique efficiently produces consistent curve networks even in the presence of large errors.
Flexible Live-Wire: Image Segmentation with Floating Anchors
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Summa, Brian; Faraj, Noura; Licorish, Cody; Pascucci, Valerio; Gutierrez, Diego and Sheffer, Alla
We introduce Flexible Live-Wire, a generalization of the Live-Wire interactive segmentation technique with floating anchors. In our approach, the user input for Live-Wire is no longer limited to the setting of pixel-level anchor nodes, but can use more general anchor sets. These sets can be of any dimension, size, or connectedness. The generality of the approach allows the design of a number of user interactions while providing the same functionality as the traditional Live-Wire. In particular, we experiment with this new flexibility by designing four novel Live-Wire interactions based on specific primitives: paint, pinch, probable, and pick anchors. These interactions are only a subset of the possibilities enabled by our generalization. Moreover, we discuss the computational aspects of this approach and provide practical solutions to alleviate any additional overhead. Finally, we illustrate our approach and new interactions through several example segmentations.
Controllable Dendritic Crystal Simulation Using Orientation Field
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Ren, Bo; Huang, Jiahui; Lin, Ming C.; Hu, Shi-Min; Gutierrez, Diego and Sheffer, Alla
Real world dendritic growths show charming structures by their exquisite balance between the symmetry and randomness in the crystal formation. Other than the variety in the natural crystals, richer visual appearance of crystals can benefit from artificially controlling of the crystal growth on its growing directions and shapes. In this paper, by introducing one extra dimension of freedom, i.e. the orientation field, into the simulation, we propose an efficient algorithm for dendritic crystal simulation that is able to reproduce arbitrary symmetry patterns with different levels of asymmetry breaking effect on general grids or meshes, including spreading on curved surfaces and growth in 3D. Flexible artistic control is also enabled in a unified manner by exploiting and guiding the orientation field in the visual simulation. We show the effectiveness of our approach by various demonstrations of simulation results.
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.
Real-Time Rendering of Wave-Optical Effects on Scratched Surfaces
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Velinov, Zdravko; Werner, Sebastian; Hullin, Matthias B.; Gutierrez, Diego and Sheffer, Alla
The visual appearance of real-world materials is characterized by surface features across many scales and has received significant attention by the graphics community for decades. Yet, even the most advanced microfacet models have difficulties faithfully recreating materials like snow, sand, brushed metal or hair that feature scale-violating glints and speckles and defy any traditional notion of filtering and level of detail. In this work, we address an important subset of such materials, namely metal and dielectric surfaces that are covered with microscopic scratches, e.g., from polishing processes or surface wear. The appearance of such surfaces features fine-scale spatial detail and iridescent colors caused by diffraction, and has only recently been successfully recreated. We adopt the scratch iridescence model, which is known for plausible results in offline Monte Carlo settings but unsuitable for real-time applications where extensive illumination sampling is prohibitively expensive. In this paper, we introduce an efficient technique for incoherently integrating the contributions of individual scratches, as well as closed-form solutions for modeling spherical and polygonal area light sources, and for the first time bring scratch iridescence within reach of real-time applications.
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.
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.
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.

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