Volume 39 (2020)

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https://diglib.eg.org/handle/10.2312/2632879

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Structure and Empathy in Visual Data Storytelling: Evaluating their Influence on Attitude
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Liem, Johannes; Perin, Charles; Wood, Jo; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
In the visualization community, it is often assumed that visual data storytelling increases memorability and engagement, making it more effective at communicating information. However, many assumptions about the efficacy of storytelling in visualization lack empirical evaluation. Contributing to an emerging body of work, we study whether selected techniques commonly used in visual data storytelling influence people's attitudes towards immigration. We compare (a) personal visual narratives designed to generate empathy; (b) structured visual narratives of aggregates of people; and (c) an exploratory visualization without narrative acting as a control condition. We conducted two crowdsourced between-subject studies comparing the three conditions, each with 300 participants. To assess the differences in attitudes between conditions, we adopted established scales from the social sciences used in the European Social Survey (ESS). Although we found some differences between conditions, the effects on people's attitudes are smaller than we expected. Our findings suggest that we need to be more careful when it comes to our expectations about the effects visual data storytelling can have on attitudes.
Next Event Estimation++: Visibility Mapping for Efficient Light Transport Simulation
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Guo, Jerry Jinfeng; Eisemann, Martin; Eisemann, Elmar; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
Monte-Carlo rendering requires determining the visibility between scene points as the most common and compute intense operation to establish paths between camera and light source. Unfortunately, many tests reveal occlusions and the corresponding paths do not contribute to the final image. In this work, we present next event estimation++ (NEE++): a visibility mapping technique to perform visibility tests in a more informed way by caching voxel to voxel visibility probabilities. We show two scenarios: Russian roulette style rejection of visibility tests and direct importance sampling of the visibility. We show applications to next event estimation and light sampling in a uni-directional path tracer, and light-subpath sampling in Bi-Directional Path Tracing. The technique is simple to implement, easy to add to existing rendering systems, and comes at almost no cost, as the required information can be directly extracted from the rendering process itself. It discards up to 80% of visibility tests on average, while reducing variance by ~20% compared to other state-of-the-art light sampling techniques with the same number of samples. It gracefully handles complex scenes with efficiency similar to Metropolis light transport techniques but with a more uniform convergence.
Memento: Localized Time‐Warping for Spatio‐Temporal Selection
(© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Solteszova, V.; Smit, N. N.; Stoppel, S.; Grüner, R.; Bruckner, S.; Benes, Bedrich and Hauser, Helwig
Interaction techniques for temporal data are often focused on affecting the spatial aspects of the data, for instance through the use of transfer functions, camera navigation or clipping planes. However, the temporal aspect of the data interaction is often neglected. The temporal component is either visualized as individual time steps, an animation or a static summary over the temporal domain. When dealing with streaming data, these techniques are unable to cope with the task of re‐viewing an interesting local spatio‐temporal event, while continuing to observe the rest of the feed. We propose a novel technique that allows users to interactively specify areas of interest in the spatio‐temporal domain. By employing a time‐warp function, we are able to slow down time, freeze time or even travel back in time, around spatio‐temporal events of interest. The combination of such a (pre‐defined) time‐warp function and brushing directly in the data to select regions of interest allows for a detailed review of temporally and spatially localized events, while maintaining an overview of the global spatio‐temporal data. We demonstrate the utility of our technique with several usage scenarios.
PEAX: Interactive Visual Pattern Search in Sequential Data Using Unsupervised Deep Representation Learning
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Lekschas, Fritz; Peterson, Brant; Haehn, Daniel; Ma, Eric; Gehlenborg, Nils; Pfister, Hanspeter; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
We present PEAX, a novel feature-based technique for interactive visual pattern search in sequential data, like time series or data mapped to a genome sequence. Visually searching for patterns by similarity is often challenging because of the large search space, the visual complexity of patterns, and the user's perception of similarity. For example, in genomics, researchers try to link patterns in multivariate sequential data to cellular or pathogenic processes, but a lack of ground truth and high variance makes automatic pattern detection unreliable. We have developed a convolutional autoencoder for unsupervised representation learning of regions in sequential data that can capture more visual details of complex patterns compared to existing similarity measures. Using this learned representation as features of the sequential data, our accompanying visual query system enables interactive feedback-driven adjustments of the pattern search to adapt to the users' perceived similarity. Using an active learning sampling strategy, PEAX collects user-generated binary relevance feedback. This feedback is used to train a model for binary classification, to ultimately find other regions that exhibit patterns similar to the search target. We demonstrate PEAX's features through a case study in genomics and report on a user study with eight domain experts to assess the usability and usefulness of PEAX. Moreover, we evaluate the effectiveness of the learned feature representation for visual similarity search in two additional user studies. We find that our models retrieve significantly more similar patterns than other commonly used techniques.
Cosserat Rod with rh-Adaptive Discretization
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Wen, Jiahao; Chen, Jiong; Nobuyuki, Umetani; Bao, Hujun; Huang, Jin; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
Rod-like one-dimensional elastic objects often exhibit complex behaviors which pose great challenges to the discretization method for pursuing a faithful simulation. By only moving a small portion of material points, the Eulerian-on-Lagrangian (EoL) method already shows great adaptivity to handle sharp contact, but it is still far from enough to reproduce rich and complex geometry details arising in simulations. In this paper, we extend the discrete configuration space by unifying all Lagrangian and EoL nodes in representation for even more adaptivity with every sample being assigned with a dynamic material coordinate. However, this great extension will immediately bring in much more redundancy in the dynamic system. Therefore, we propose additional energy to control the spatial distribution of all material points, seeking to equally space them with respect to a curvature-based density field as a monitor. This flexible approach can effectively constrain the motion of material points to resolve numerical degeneracy, while simultaneously enables them to notably slide inside the parametric domain to account for the shape parameterization. Besides, to accurately respond to sharp contact, our method can also insert or remove nodes online and adjust the energy stiffness to suppress possible jittering artifacts that could be excited in a stiff system. As a result of this hybrid rh-adaption, our proposed method is capable of reproducing many realistic rod dynamics, such as excessive bending, twisting and knotting while only using a limited number of elements.
Practical Fabrication of Discrete Chebyshev Nets
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Liu, Hao-Yu; Liu, Zhong-Yuan; Zhao, Zheng-Yu; Liu, Ligang; Fu, Xiao-Ming; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
We propose a computational and practical technique to allow home users to fabricate discrete Chebyshev nets for various 3D models. The success of our method relies on two key components. The first one is a novel and simple method to approximate discrete integrable, unit-length, and angle-bounded frame fields, used to model discrete Chebyshev nets. Central to our field generation process is an alternating algorithm that takes turns executing one pass to enforce integrability and another pass to approach unit length while bounding angles. The second is a practical fabrication specification. The discrete Chebyshev net is first partitioned into a set of patches to facilitate manufacturing. Then, each patch is assigned a specification on pulling, bend, and fold to fit the nets. We demonstrate the capability and feasibility of our method in various complex models.
Fully Convolutional Graph Neural Networks for Parametric Virtual Try-On
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Vidaurre, Raquel; Santesteban, Igor; Garces, Elena; Casas, Dan; Bender, Jan and Popa, Tiberiu
We present a learning-based approach for virtual try-on applications based on a fully convolutional graph neural network. In contrast to existing data-driven models, which are trained for a specific garment or mesh topology, our fully convolutional model can cope with a large family of garments, represented as parametric predefined 2D panels with arbitrary mesh topology, including long dresses, shirts, and tight tops. Under the hood, our novel geometric deep learning approach learns to drape 3D garments by decoupling the three different sources of deformations that condition the fit of clothing: garment type, target body shape, and material. Specifically, we first learn a regressor that predicts the 3D drape of the input parametric garment when worn by a mean body shape. Then, after a mesh topology optimization step where we generate a sufficient level of detail for the input garment type, we further deform the mesh to reproduce deformations caused by the target body shape. Finally, we predict fine-scale details such as wrinkles that depend mostly on the garment material. We qualitatively and quantitatively demonstrate that our fully convolutional approach outperforms existing methods in terms of generalization capabilities and memory requirements, and therefore it opens the door to more general learning-based models for virtual try-on applications.
Probabilistic Character Motion Synthesis using a Hierarchical Deep Latent Variable Model
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Ghorbani, Saeed; Wloka, Calden; Etemad, Ali; Brubaker, Marcus A.; Troje, Nikolaus F.; Bender, Jan and Popa, Tiberiu
We present a probabilistic framework to generate character animations based on weak control signals, such that the synthesized motions are realistic while retaining the stochastic nature of human movement. The proposed architecture, which is designed as a hierarchical recurrent model, maps each sub-sequence of motions into a stochastic latent code using a variational autoencoder extended over the temporal domain. We also propose an objective function which respects the impact of each joint on the pose and compares the joint angles based on angular distance. We use two novel quantitative protocols and human qualitative assessment to demonstrate the ability of our model to generate convincing and diverse periodic and non-periodic motion sequences without the need for strong control signals.
Physically Based Simulation and Rendering of Urban Thermography
(© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Aguerre, José Pedro; García‐Nevado, Elena; Acuña Paz y Miño, Jairo; Fernández, Eduardo; Beckers, Benoit; Benes, Bedrich and Hauser, Helwig
Urban thermography is a non‐invasive measurement technique commonly used for building diagnosis and energy efficiency evaluation. The physical interpretation of thermal images is a challenging task because they do not necessarily depict the real temperature of the surfaces, but one estimated from the measured incoming radiation. In this sense, the computational rendering of a thermal image can be useful to understand the results captured in a measurement campaign. The computer graphics community has proposed techniques for light rendering that are used for its thermal counterpart. In this work, a physically based simulation methodology based on a combination of the finite element method (FEM) and ray tracing is presented. The proposed methods were tested using a highly detailed urban geometry. Directional emissivity models, glossy reflectivity functions and importance sampling were used to render thermal images. The simulation results were compared with a set of measured thermograms, showing good agreement between them.
UV-free Texturing using Sparse Voxel DAGs
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Dolonius, Dan; Sintorn, Erik; Assarsson, Ulf; Panozzo, Daniele and Assarsson, Ulf
An application may have to load an unknown 3D model and, for enhanced realistic rendering, precompute values over the surface domain, such as light maps, ambient occlusion, or other global-illumination parameters. High-quality uv-unwrapping has several problems, such as seams, distortions, and wasted texture space. Additionally, procedurally generated scene content, perhaps on the fly, can make manual uv unwrapping impossible. Even when artist manipulation is feasible, good uv layouts can require expertise and be highly labor intensive. This paper investigates how to use Sparse Voxel DAGs (or DAGs for short) as one alternative to avoid uv mapping. The result is an algorithm enabling high compression ratios of both voxel structure and colors, which can be important for a baked scene to fit in GPU memory. Specifically, we enable practical usage for an automatic system by targeting efficient real-time mipmap filtering using compressed textures and adding support for individual mesh voxelizations and resolutions in the same DAG. Furthermore, the latter increases the texture-compression ratios by up to 32% compared to using one global voxelization, DAG compression by 10-15% compared to using a DAG per mesh, and reduces color-bleeding problems for large mipmap filter sizes. The voxel-filtering is more costly than standard hardware 2D-texture filtering. However, for full HD with deferred shading, it is optimized down to 2:5 +/- 0:5 ms for a custom multisampling filtering (e.g., targeted for minification of low-frequency textures) and 5 +/- 2 ms for quad-linear mipmap filtering (e.g., for high-frequency textures). Multiple textures sharing voxelization can amortize the majority of this cost. Hence, these numbers involve 1-3 textures per pixel (Fig. 1c).
A Multi-Person Selfie System via Augmented Reality
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Lin, Jie; Yang, Chuan-Kai; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
The limited length of a selfie stick always poses the problem of distortion in a selfie, in spite of the prevalence of selfie stick in recent years.We propose a technique, based on modifying existing augmented reality technology, to support the selfie of multiple persons, through properly aligning different photographing processes. It can be shown that our technique helps avoiding the common distortion drawback of using a selfie stick, and facilitates the composition process of a group photo. It can also be used to create some special effects, including creating an illusion of having multiple appearances of a person.
Efficient Homology‐Preserving Simplification of High‐Dimensional Simplicial Shapes
(© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Fellegara, Riccardo; Iuricich, Federico; De Floriani, Leila; Fugacci, Ulderico; Benes, Bedrich and Hauser, Helwig
Simplicial complexes are widely used to discretize shapes. In low dimensions, a 3D shape is represented by discretizing its boundary surface, encoded as a triangle mesh, or by discretizing the enclosed volume, encoded as a tetrahedral mesh. High‐dimensional simplicial complexes have recently found their application in topological data analysis. Topological data analysis aims at studying a point cloud P, possibly embedded in a high‐dimensional metric space, by investigating the topological characteristics of the simplicial complexes built on P. Analysing such complexes is not feasible due to their size and dimensions. To this aim, the idea of simplifying a complex while preserving its topological features has been proposed in the literature. Here, we consider the problem of efficiently simplifying simplicial complexes in arbitrary dimensions. We provide a new definition for the edge contraction operator, based on a top‐based data structure, with the objective of preserving structural aspects of a simplicial shape (i.e., its homology), and a new algorithm for verifying the link condition on a top‐based representation. We implement the simplification algorithm obtained by coupling the new edge contraction and the link condition on a specific top‐based data structure, that we use to demonstrate the scalability of our approach.
A Survey on Visual Traffic Simulation: Models, Evaluations, and Applications in Autonomous Driving
(© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Chao, Qianwen; Bi, Huikun; Li, Weizi; Mao, Tianlu; Wang, Zhaoqi; Lin, Ming C.; Deng, Zhigang; Benes, Bedrich and Hauser, Helwig
Virtualized traffic via various simulation models and real‐world traffic data are promising approaches to reconstruct detailed traffic flows. A variety of applications can benefit from the virtual traffic, including, but not limited to, video games, virtual reality, traffic engineering and autonomous driving. In this survey, we provide a comprehensive review on the state‐of‐the‐art techniques for traffic simulation and animation. We start with a discussion on three classes of traffic simulation models applied at different levels of detail. Then, we introduce various data‐driven animation techniques, including existing data collection methods, and the validation and evaluation of simulated traffic flows. Next, we discuss how traffic simulations can benefit the training and testing of autonomous vehicles. Finally, we discuss the current states of traffic simulation and animation and suggest future research directions.
Personalized Hand Modeling from Multiple Postures with Multi-View Color Images
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Wang, Yangang; Rao, Ruting; Zou, Changqing; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
Personalized hand models can be utilized to synthesize high quality hand datasets, provide more possible training data for deep learning and improve the accuracy of hand pose estimation. In recent years, parameterized hand models, e.g., MANO, are widely used for obtaining personalized hand models. However, due to the low resolution of existing parameterized hand models, it is still hard to obtain high-fidelity personalized hand models. In this paper, we propose a new method to estimate personalized hand models from multiple hand postures with multi-view color images. The personalized hand model is represented by a personalized neutral hand, and multiple hand postures. We propose a novel optimization strategy to estimate the neutral hand from multiple hand postures. To demonstrate the performance of our method, we have built a multi-view system and captured more than 35 people, and each of them has 30 hand postures.We hope the estimated hand models can boost the research of highfidelity parameterized hand modeling in the future. All the hand models are publicly available on www.yangangwang.com.
SiamesePointNet: A Siamese Point Network Architecture for Learning 3D Shape Descriptor
(© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Zhou, J.; Wang, M. J.; Mao, W. D.; Gong, M. L.; Liu, X. P.; Benes, Bedrich and Hauser, Helwig
We present a novel deep learning approach to extract point‐wise descriptors directly on 3D shapes by introducing Siamese Point Networks, which contain a global shape constraint module and a feature transformation operator. Such geometric descriptor can be used in a variety of shape analysis problems such as 3D shape dense correspondence, key point matching and shape‐to‐scan matching. The descriptor is produced by a hierarchical encoder–decoder architecture that is trained to map geometrically and semantically similar points close to one another in descriptor space. Benefiting from the additional shape contrastive constraint and the hierarchical local operator, the learned descriptor is highly aware of both the global context and local context. In addition, a feature transformation operation is introduced in the end of our networks to transform the point features to a compact descriptor space. The feature transformation can make the descriptors extracted by our networks unaffected by geometric differences in shapes. Finally, an N‐tuple loss is used to train all the point descriptors on a complete 3D shape simultaneously to obtain point‐wise descriptors. The proposed Siamese Point Networks are robust to many types of perturbations such as the Gaussian noise and partial scan. In addition, we demonstrate that our approach improves state‐of‐the‐art results on the BHCP benchmark.
A Survey of Visual Analytics for Public Health
(© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Preim, Bernhard; Lawonn, Kai; Benes, Bedrich and Hauser, Helwig
We describe visual analytics solutions aiming to support public health professionals, and thus, preventive measures. Prevention aims at advocating behaviour and policy changes likely to improve human health. Public health strives to limit the outbreak of acute diseases as well as the reduction of chronic diseases and injuries. For this purpose, data are collected to identify trends in human health, to derive hypotheses, e.g. related to risk factors, and to get insights in the data and the underlying phenomena. Most public health data have a temporal character. Moreover, the spatial character, e.g. spatial clustering of diseases, needs to be considered for decision‐making. Visual analytics techniques involve (subspace) clustering, interaction techniques to identify relevant subpopulations, e.g. being particularly vulnerable to diseases, imputation of missing values, visual queries as well as visualization and interaction techniques for spatio‐temporal data. We describe requirements, tasks and visual analytics techniques that are widely used in public health before going into detail with respect to applications. These include outbreak surveillance and epidemiology research, e.g. cancer epidemiology. We classify the solutions based on the visual analytics techniques employed. We also discuss gaps in the current state of the art and resulting research opportunities in a research agenda to advance visual analytics support in public health.
A Cross‐Dimension Annotations Method for 3D Structural Facial Landmark Extraction
(© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Gong, Xun; Chen, Ping; Zhang, Zhemin; Chen, Ke; Xiang, Yue; Li, Xin; Benes, Bedrich and Hauser, Helwig
Recent methods for 2D facial landmark localization perform well on close‐to‐frontal faces, but 2D landmarks are insufficient to represent 3D structure of a facial shape. For applications that require better accuracy, such as facial motion capture and 3D shape recovery, 3DA‐2D (2D Projections of 3D Facial Annotations) is preferred. Inferring the 3D structure from a single image is an problem whose accuracy and robustness are not always guaranteed. This paper aims to solve accurate 2D facial landmark localization and the transformation between 2D and 3DA‐2D landmarks. One way to increase the accuracy is to input more precisely annotated facial images. The traditional cascaded regressions cannot effectively handle large or noisy training data sets. In this paper, we propose a Mini‐Batch Cascaded Regressions (MBCR) method that can iteratively train a robust model from a large data set. Benefiting from the incremental learning strategy and a small learning rate, MBCR is robust to noise in training data. We also propose a new Cross‐Dimension Annotations Conversion (CDAC) method to map facial landmarks from 2D to 3DA‐2D coordinates and vice versa. The experimental results showed that CDAC combined with MBCR outperforms the‐state‐of‐the‐art methods in 3DA‐2D facial landmark localization. Moreover, CDAC can run efficiently at up to 110 on a 3.4 GHz‐CPU workstation. Thus, CDAC provides a solution to transform existing 2D alignment methods into 3DA‐2D ones without slowing down the speed. Training and testing code as well as the data set can be downloaded from https://github.com/SWJTU‐3DVision/CDAC.
Multi-scale Information Assembly for Image Matting
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Qiao, Yu; Liu, Yuhao; Zhu, Qiang; Yang, Xin; Wang, Yuxin; Zhang, Qiang; Wei, Xiaopeng; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
Image matting is a long-standing problem in computer graphics and vision, mostly identified as the accurate estimation of the foreground in input images.We argue that the foreground objects can be represented by different-level information, including the central bodies, large-grained boundaries, refined details, etc. Based on this observation, in this paper, we propose a multi-scale information assembly framework (MSIA-matte) to pull out high-quality alpha mattes from single RGB images. Technically speaking, given an input image, we extract advanced semantics as our subject content and retain initial CNN features to encode different-level foreground expression, then combine them by our well-designed information assembly strategy. Extensive experiments can prove the effectiveness of the proposed MSIA-matte, and we can achieve state-of-the-art performance compared to most existing matting networks.
Illumination-Guided Furniture Layout Optimization
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Vitsas, Nick; Papaioannou, Georgios; Gkaravelis, Anastasios; Vasilakis, Andreas-Alexandros; Panozzo, Daniele and Assarsson, Ulf
Lighting plays a very important role in interior design. However, in the specific problem of furniture layout recommendation, illumination has been either neglected or addressed with empirical or very simplified solutions. The effectiveness of a particular layout in its expected task performance can be greatly affected by daylighting and artificial illumination in a non-trivial manner. In this paper, we introduce a robust method for furniture layout optimization guided by illumination constraints. The method takes into account all dominant light sources, such as sun light, skylighting and fixtures, while also being able to handle movable light emitters. For this task, the method introduces multiple generic illumination constraints and physically-based light transport estimators, operating alongside typical geometric design guidelines, in a unified manner. We demonstrate how to produce furniture arrangements that comply with important safety, comfort and efficiency illumination criteria, such as glare suppression, under complex light-environment interactions, which are very hard to handle using empirical or simplified models.
Sunspot Plots: Model-based Structure Enhancement for Dense Scatter Plots
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Trautner, Thomas; Bolte, Fabian; Stoppel, Sergej; Bruckner, Stefan; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Scatter plots are a powerful and well-established technique for visualizing the relationships between two variables as a collection of discrete points. However, especially when dealing with large and dense data, scatter plots often exhibit problems such as overplotting, making the data interpretation arduous. Density plots are able to overcome these limitations in highly populated regions, but fail to provide accurate information of individual data points. This is particularly problematic in sparse regions where the density estimate may not provide a good representation of the underlying data. In this paper, we present sunspot plots, a visualization technique that communicates dense data as a continuous data distribution, while preserving the discrete nature of data samples in sparsely populated areas. We furthermore demonstrate the advantages of our approach on typical failure cases of scatter plots within synthetic and real-world data sets and validate its effectiveness in a user study.

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