Volume 37 (2018)

Permanent URI for this community

https://diglib.eg.org/handle/10.2312/2632118

Browse

Now showing 1 - 20 of 253

Rendering and Extracting Extremal Features in 3D Fields
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Kindlmann, Gordon L.; Chiw, Charisee; Huynh, Tri; Gyulassy, Attila; Reppy, John; Bremer, Peer-Timo; Jeffrey Heer and Heike Leitte and Timo Ropinski
Visualizing and extracting three-dimensional features is important for many computational science applications, each with their own feature definitions and data types. While some are simple to state and implement (e.g. isosurfaces), others require more complicated mathematics (e.g. multiple derivatives, curvature, eigenvectors, etc.). Correctly implementing mathematical definitions is difficult, so experimenting with new features requires substantial investments. Furthermore, traditional interpolants rarely support the necessary derivatives, and approximations can reduce numerical stability. Our new approach directly translates mathematical notation into practical visualization and feature extraction, with minimal mental and implementation overhead. Using a mathematically expressive domain-specific language, Diderot, we compute direct volume renderings and particlebased feature samplings for a range of mathematical features. Non-expert users can experiment with feature definitions without any exposure to meshes, interpolants, derivative computation, etc. We demonstrate high-quality results on notoriously difficult features, such as ridges and vortex cores, using working code simple enough to be presented in its entirety.
Visualization of 4D Vector Field Topology
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Hofmann, Lutz; Rieck, Bastian; Sadlo, Filip; Jeffrey Heer and Heike Leitte and Timo Ropinski
In this paper, we present an approach to the topological analysis of four-dimensional vector fields. In analogy to traditional 2D and 3D vector field topology, we provide a classification and visual representation of critical points, together with a technique for extracting their invariant manifolds. For effective exploration of the resulting four-dimensional structures, we present a 4D camera that provides concise representation by exploiting projection degeneracies, and a 4D clipping approach that avoids self-intersection in the 3D projection. We exemplify the properties and the utility of our approach using specific synthetic cases.
HairControl: A Tracking Solution for Directable Hair Simulation
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Milliez, Antoine; Sumner, Robert W.; Gross, Markus; Thomaszewski, Bernhard; Thuerey, Nils and Beeler, Thabo
We present a method for adding artistic control to physics-based hair simulation. Taking as input an animation of a coarse set of guide hairs, we constrain a subsequent higher-resolution simulation of detail hairs to follow the input motion in a spatially-averaged sense. The resulting high-resolution motion adheres to the artistic intent, but is enhanced with detailed deformations and dynamics generated by physics-based simulation. The technical core of our approach is formed by a set of tracking constraints, requiring the center of mass of a given subset of detail hair to maintain its position relative to a reference point on the corresponding guide hair. As a crucial element of our formulation, we introduce the concept of dynamicallychanging constraint targets that allow reference points to slide along the guide hairs to provide sufficient flexibility for natural deformations. We furthermore propose to regularize the null space of the tracking constraints based on variance minimization, effectively controlling the amount of spread in the hair. We demonstrate the ability of our tracking solver to generate directable yet natural hair motion on a set of targeted experiments and show its application to production-level animations.
Learning Physically Based Humanoid Climbing Movements
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Naderi, Kourosh; Babadi, Amin; Hämäläinen, Perttu; Thuerey, Nils and Beeler, Thabo
We propose a novel learning-based solution for motion planning of physically-based humanoid climbing that allows for fast and robust planning of complex climbing strategies and movements, including extreme movements such as jumping. Similar to recent previous work, we combine a high-level graph-based path planner with low-level sampling-based optimization of climbing moves. We contribute through showing that neural network models of move success probability, effortfulness, and control policy can make both the high-level and low-level components more efficient and robust. The models can be trained through random simulation practice without any data. The models also eliminate the need for laboriously hand-tuned heuristics for graph search. As a result, we are able to efficiently synthesize climbing sequences involving dynamic leaps and one-hand swings, i.e. there are no limits to the movement complexity or the number of limbs allowed to move simultaneously. Our supplemental video also provides some comparisons between our AI climber and a real human climber.
Semantic Reconstruction: Reconstruction of Semantically Segmented 3D Meshes via Volumetric Semantic Fusion
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Jeon, Junho; Jung, Jinwoong; Kim, Jungeon; Lee, Seungyong; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
Semantic segmentation partitions a given image or 3D model of a scene into semantically meaning parts and assigns predetermined labels to the parts. With well-established datasets, deep networks have been successfully used for semantic segmentation of RGB and RGB-D images. On the other hand, due to the lack of annotated large-scale 3D datasets, semantic segmentation for 3D scenes has not yet been much addressed with deep learning. In this paper, we present a novel framework for generating semantically segmented triangular meshes of reconstructed 3D indoor scenes using volumetric semantic fusion in the reconstruction process. Our method integrates the results of CNN-based 2D semantic segmentation that is applied to the RGB-D stream used for dense surface reconstruction. To reduce the artifacts from noise and uncertainty of single-view semantic segmentation, we introduce adaptive integration for the volumetric semantic fusion and CRF-based semantic label regularization. With these methods, our framework can easily generate a high-quality triangular mesh of the reconstructed 3D scene with dense (i.e., per-vertex) semantic labels. Extensive experiments demonstrate that our semantic segmentation results of 3D scenes achieves the state-of-the-art performance compared to the previous voxel-based and point cloud-based methods.
Easy Generation of Facial Animation Using Motion Graphs
(© 2018 The Eurographics Association and John Wiley & Sons Ltd., 2018) Serra, J.; Cetinaslan, O.; Ravikumar, S.; Orvalho, V.; Cosker, D.; Chen, Min and Benes, Bedrich
Facial animation is a time‐consuming and cumbersome task that requires years of experience and/or a complex and expensive set‐up. This becomes an issue, especially when animating the multitude of secondary characters required, e.g. in films or video‐games. We address this problem with a novel technique that relies on motion graphs to represent a landmarked database. Separate graphs are created for different facial regions, allowing a reduced memory footprint compared to the original data. The common poses are identified using a Euclidean‐based similarity metric and merged into the same node. This process traditionally requires a manually chosen threshold, however, we simplify it by optimizing for the desired graph compression. Motion synthesis occurs by traversing the graph using Dijkstra's algorithm, and coherent noise is introduced by swapping some path nodes with their neighbours. Expression labels, extracted from the database, provide the control mechanism for animation. We present a way of creating facial animation with reduced input that automatically controls timing and pose detail. Our technique easily fits within video‐game and crowd animation contexts, allowing the characters to be more expressive with less effort. Furthermore, it provides a starting point for content creators aiming to bring more life into their characters.Facial animation is a time‐consuming and cumbersome task that requires years of experience and/or a complex and expensive set‐up. This becomes an issue, especially when animating the multitude of secondary characters required, e.g. in films or video‐games. We address this problem with a novel technique that relies on motion graphs to represent a landmarked database. Separate graphs are created for different facial regions, allowing a reduced memory footprint compared to the original data. This process traditionally requires a manually chosen threshold, however, we simplify it by optimizing for the desired graph compression. Motion synthesis occurs by traversing the graph, with coherent noise introduced by varying the optimal path that connects the desired nodes. Expression labels, extracted from the database, provide an intuitive control mechanism for animation. Our technique easily fits within video‐game and crowd animation contexts, allowing the characters to be more expressive with less effort.
EUROGRAPHICS 2018: CGF 37-2 Frontmatter
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Gutierrez, Diego; Sheffer, Alla; Gutierrez, Diego; Sheffer, Alla
-
A Practical Approach to Physically-Based Reproduction of Diffusive Cosmetics
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Kim, Goanghun; Ko, Hyeong-Seok; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
In this paper, we introduce so-called the bSX method as a new way to utilize the Kubelka-Munk (K-M) model. Assuming the material is completely diffusive, the K-M model gives the reflectance and transmittance of the material from the observation of the material applied on a backing, where the observation includes the thickness of the material application. By rearranging the original K-M equation, we propose that the reflectance and transmittance can be calculated without knowing the thickness. This is a practically useful contribution. Based on the above finding, we develop the bSX method which can (1) capture the material specific parameters from the two photos - taken before and after the material application, and (2) reproduce its effect on a novel backing. We experimented the proposed method in various cases related to virtual cosmetic try-on, which include (1) capture from a single color backing, (2) capture from human skin backing, (3) reproduction of varying thickness effect, (4) reproduction of multi-layer cosmetic application effect, (5) applying the proposed method to makeup transfer. Compared to previous image-based makeup transfer methods, the bSX method reproduces the feel of the cosmetics more accurately.
Principal Geodesic Analysis in the Space of Discrete Shells
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Heeren, Behrend; Zhang, Chao; Rumpf, Martin; Smith, William; Ju, Tao and Vaxman, Amir
Important sources of shape variability, such as articulated motion of body models or soft tissue dynamics, are highly nonlinear and are usually superposed on top of rigid body motion which must be factored out. We propose a novel, nonlinear, rigid body motion invariant Principal Geodesic Analysis (PGA) that allows us to analyse this variability, compress large variations based on statistical shape analysis and fit a model to measurements. For given input shape data sets we show how to compute a low dimensional approximating submanifold on the space of discrete shells, making our approach a hybrid between a physical and statistical model. General discrete shells can be projected onto the submanifold and sparsely represented by a small set of coefficients. We demonstrate two specific applications: model-constrained mesh editing and reconstruction of a dense animated mesh from sparse motion capture markers using the statistical knowledge as a prior.
Subdivision Schemes With Optimal Bounded Curvature Near Extraordinary Vertices
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Ma, Yue; Ma, Weiyin; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
We present a novel method to construct subdivision stencils near extraordinary vertices with limit surfaces having optimal bounded curvature at extraordinary positions. With the proposed method, subdivision stencils for newly inserted and updated vertices near extraordinary vertices are first constructed to ensure subdivision with G1 continuity and bounded curvature at extraordinary positions. The remaining degrees of freedom of the constructed subdivision stencils are further used to optimize the eigenbasis functions corresponding to the subsubdominant eigenvalues of the subdivision with respect to G2 continuity constraints. We demonstrate the method by replacing subdivision stencils near extraordinary vertices for Catmull-Clark subdivision and compare the results with the original Catmull-Clark subdivision and previous tuning schemes known with small curvature variation near extraordinary positions. The results show that the proposed method produces subdivision schemes with better or comparable curvature behavior around extraordinary vertices with comparatively simple subdivision stencils.
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.
Visualizing Multidimensional Data with Order Statistics
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Raj, Mukund; Whitaker, Ross T.; Jeffrey Heer and Heike Leitte and Timo Ropinski
Multidimensional data sets are common in many domains, and dimensionality reduction methods that determine a lower dimensional embedding are widely used for visualizing such data sets. This paper presents a novel method to project data onto a lower dimensional space by taking into account the order statistics of the individual data points, which are quantified by their depth or centrality in the overall set. Thus, in addition to conveying relative distances in the data, the proposed method also preserves the order statistics, which are often lost or misrepresented by existing visualization methods. The proposed method entails a modification of the optimization objective of conventional multidimensional scaling (MDS) by introducing a term that penalizes discrepancies between centrality structures in the original space and the embedding. We also introduce two strategies for visualizing lower dimensional embeddings of multidimensional data that takes advantage of the coherent representation of centrality provided by the proposed projection method. We demonstrate the effectiveness of our visualization with comparisons on different kinds of multidimensional data, including categorical and multimodal, from a variety of domains such as botany and health care.
Effective Characterization of Relief Patterns
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Giachetti, Andrea; Ju, Tao and Vaxman, Amir
In this paper, we address the problem of characterizing relief patterns over surface meshes independently on the underlying shape. We propose to tackle the problem by estimating local invariant features and encoding them using the Improved Fisher Vector technique, testing both features estimated on 3D meshes and local descriptors estimated on raster images created by encoding local surface properties (e.g. mean curvature) over a surface parametrization. We compare the robustness of the obtained descriptors against noise and surface bending and evaluate retrieval performances on a specific benchmark proposed in a track of the Eurographics Shape REtrieval Contest 2017. Results show that, with the proposed framework, it is possible to obtain retrieval results largely improving the state of the art and that the image-based approach is still effective when the underlying surface is heavily deformed.
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.
2018 Cover Image: Thingi10K
(© 2018 The Eurographics Association and John Wiley & Sons Ltd., 2018) Zhou, Qingnan; Jacobson, Alec; Chen, Min and Benes, Bedrich
Kernel Functional Maps
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Wang, Larry; Gehre, Anne; Bronstein, Michael M.; Solomon, Justin; Ju, Tao and Vaxman, Amir
Functional maps provide a means of extracting correspondences between surfaces using linear-algebraic machinery. While the functional framework suggests efficient algorithms for map computation, the basic technique does not incorporate the intuition that pointwise modifications of a descriptor function (e.g. composition of a descriptor and a nonlinearity) should be preserved under the mapping; the end result is that the basic functional maps problem can be underdetermined without regularization or additional assumptions on the map. In this paper, we show how this problem can be addressed through kernelization, in which descriptors are lifted to higher-dimensional vectors or even infinite-length sequences of values. The key observation is that optimization problems for functional maps only depend on inner products between descriptors rather than descriptor values themselves. These inner products can be evaluated efficiently through use of kernel functions. In addition to deriving a kernelized version of functional maps including a recent extension in terms of pointwise multiplication operators, we provide an efficient conjugate gradient algorithm for optimizing our generalized problem as well as a strategy for low-rank estimation of kernel matrices through the Nyström approximation.
The State of the Art in Sentiment Visualization
(© 2018 The Eurographics Association and John Wiley & Sons Ltd., 2018) Kucher, Kostiantyn; Paradis, Carita; Kerren, Andreas; Chen, Min and Benes, Bedrich
Visualization of sentiments and opinions extracted from or annotated in texts has become a prominent topic of research over the last decade. From basic pie and bar charts used to illustrate customer reviews to extensive visual analytics systems involving novel representations, sentiment visualization techniques have evolved to deal with complex multidimensional data sets, including temporal, relational and geospatial aspects. This contribution presents a survey of sentiment visualization techniques based on a detailed categorization. We describe the background of sentiment analysis, introduce a categorization for sentiment visualization techniques that includes 7 groups with 35 categories in total, and discuss 132 techniques from peer‐reviewed publications together with an interactive web‐based survey browser. Finally, we discuss insights and opportunities for further research in sentiment visualization. We expect this survey to be useful for visualization researchers whose interests include sentiment or other aspects of text data as well as researchers and practitioners from other disciplines in search of efficient visualization techniques applicable to their tasks and data.Visualization of sentiments and opinions extracted from or annotated in texts has become a prominent topic of research over the last decade. From basic pie and bar charts used to illustrate customer reviews to extensive visual analytics systems involving novel representations, sentiment visualization techniques have evolved to deal with complex multidimensional data sets, including temporal, relational and geospatial aspects. This contribution presents a survey of sentiment visualization techniques based on a detailed categorization. We describe the background of sentiment analysis, introduce a categorization for sentiment visualization techniques that includes 7 groups with 35 categories in total, and discuss 132 techniques from peer‐reviewed publications together with an interactive web‐based survey browser. Finally, we discuss insights and opportunities for further research in sentiment visualization.
Direct Position‐Based Solver for Stiff Rods
(© 2018 The Eurographics Association and John Wiley & Sons Ltd., 2018) Deul, Crispin; Kugelstadt, Tassilo; Weiler, Marcel; Bender, Jan; Chen, Min and Benes, Bedrich
In this paper, we present a novel direct solver for the efficient simulation of stiff, inextensible elastic rods within the position‐based dynamics (PBD) framework. It is based on the XPBD algorithm, which extends PBD to simulate elastic objects with physically meaningful material parameters. XPBD approximates an implicit Euler integration and solves the system of non‐linear equations using a non‐linear Gauss–Seidel solver. However, this solver requires many iterations to converge for complex models and if convergence is not reached, the material becomes too soft. In contrast, we use Newton iterations in combination with our direct solver to solve the non‐linear equations which significantly improves convergence by solving all constraints of an acyclic structure (tree), simultaneously. Our solver only requires a few Newton iterations to achieve high stiffness and inextensibility. We model inextensible rods and trees using rigid segments connected by constraints. Bending and twisting constraints are derived from the well‐established Cosserat model. The high performance of our solver is demonstrated in highly realistic simulations of rods consisting of multiple 10 000 segments. In summary, our method allows the efficient simulation of stiff rods in the PBD framework with a speedup of two orders of magnitude compared to the original XPBD approach.We present a novel direct solver for the efficient simulation of stiff, inextensible elastic rods. It is based on the XPBD algorithm, which extends Position‐Based Dynamics to simulate elastic objects with physically meaningful material parameters. However, the non‐linear Gauss‐Seidel solver of XPBD requires many iterations to converge for complex models and if convergence is not reached, the material becomes too soft. In contrast, we use Newton iterations in combination with our direct solver which significantly improves convergence by solving all constraints of an acyclic structure simultaneously. We model rods using rigid segments connected by constraints. Bending and twisting constraints are derived from the Cosserat model. The high performance of our solver allows the simulation of rods consisting of multiple 10 000 segments with a speedup of two orders of magnitude compared to the original XPBD approach.
Quad-Based Fourier Transform for Efficient Diffraction Synthesis
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Scandolo, Leonardo; Lee, Sungkil; Eisemann, Elmar; Jakob, Wenzel and Hachisuka, Toshiya
Far-field diffraction can be evaluated using the Discrete Fourier Transform (DFT) in image space but it is costly due to its dense sampling. We propose a technique based on a closed-form solution of the continuous Fourier transform for simple vector primitives (quads) and propose a hierarchical and progressive evaluation to achieve real-time performance. Our method is able to simulate diffraction effects in optical systems and can handle varying visibility due to dynamic light sources. Furthermore, it seamlessly extends to near-field diffraction. We show the benefit of our solution in various applications, including realistic real-time glare and bloom rendering.
Visualizing Expanded Query Results
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Mazurek, Michael; Waldner, Manuela; Jeffrey Heer and Heike Leitte and Timo Ropinski
When performing queries in web search engines, users often face difficulties choosing appropriate query terms. Search engines therefore usually suggest a list of expanded versions of the user query to disambiguate it or to resolve potential term mismatches. However, it has been shown that users find it difficult to choose an expanded query from such a list. In this paper, we describe the adoption of set-based text visualization techniques to visualize how query expansions enrich the result space of a given user query and how the result sets relate to each other. Our system uses a linguistic approach to expand queries and topic modeling to extract the most informative terms from the results of these queries. In a user study, we compare a common text list of query expansion suggestions to three set-based text visualization techniques adopted for visualizing expanded query results - namely, Compact Euler Diagrams, Parallel Tag Clouds, and a List View - to resolve ambiguous queries using interactive query expansion. Our results show that text visualization techniques do not increase retrieval efficiency, precision, or recall. Overall, users rate Parallel Tag Clouds visualizing key terms of the expanded query space lowest. Based on the results, we derive recommendations for visualizations of query expansion results, text visualization techniques in general, and discuss alternative use cases of set-based text visualization techniques in the context of web search.

Browse

Browsing Volume 37 (2018) by Issue Date

Results Per Page

Sort Options