Volume 40 (2021)

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https://diglib.eg.org/handle/10.2312/2633014

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Interactive Photo Editing on Smartphones via Intrinsic Decomposition
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Shekhar, Sumit; Reimann, Max; Mayer, Maximilian; Semmo, Amir; Pasewaldt, Sebastian; Döllner, Jürgen; Trapp, Matthias; Mitra, Niloy and Viola, Ivan
Intrinsic decomposition refers to the problem of estimating scene characteristics, such as albedo and shading, when one view or multiple views of a scene are provided. The inverse problem setting, where multiple unknowns are solved given a single known pixel-value, is highly under-constrained. When provided with correlating image and depth data, intrinsic scene decomposition can be facilitated using depth-based priors, which nowadays is easy to acquire with high-end smartphones by utilizing their depth sensors. In this work, we present a system for intrinsic decomposition of RGB-D images on smartphones and the algorithmic as well as design choices therein. Unlike state-of-the-art methods that assume only diffuse reflectance, we consider both diffuse and specular pixels. For this purpose, we present a novel specularity extraction algorithm based on a multi-scale intensity decomposition and chroma inpainting. At this, the diffuse component is further decomposed into albedo and shading components. We use an inertial proximal algorithm for non-convex optimization (iPiano) to ensure albedo sparsity. Our GPUbased visual processing is implemented on iOS via the Metal API and enables interactive performance on an iPhone 11 Pro. Further, a qualitative evaluation shows that we are able to obtain high-quality outputs. Furthermore, our proposed approach for specularity removal outperforms state-of-the-art approaches for real-world images, while our albedo and shading layer decomposition is faster than the prior work at a comparable output quality. Manifold applications such as recoloring, retexturing, relighting, appearance editing, and stylization are shown, each using the intrinsic layers obtained with our method and/or the corresponding depth data.
MultiResGNet: Approximating Nonlinear Deformation via Multi-Resolution Graphs
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Li, Tianxing; Shi, Rui; Kanai, Takashi; Mitra, Niloy and Viola, Ivan
This paper presents a graph-learning-based, powerfully generalized method for automatically generating nonlinear deformation for characters with an arbitrary number of vertices. Large-scale character datasets with a significant number of poses are normally required for training to learn such automatic generalization tasks. There are two key contributions that enable us to address this challenge while making our network generalized to achieve realistic deformation approximation. First, after the automatic linear-based deformation step, we encode the roughly deformed meshes by constructing graphs where we propose a novel graph feature representation method with three descriptors to represent meshes of arbitrary characters in varying poses. Second, we design a multi-resolution graph network (MultiResGNet) that takes the constructed graphs as input, and end-to-end outputs the offset adjustments of each vertex. By processing multi-resolution graphs, general features can be better extracted, and the network training no longer heavily relies on large amounts of training data. Experimental results show that the proposed method achieves better performance than prior studies in deformation approximation for unseen characters and poses.
Levitating Rigid Objects with Hidden Rods and Wires
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Kushner, Sarah; Ulinski, Risa; Singh, Karan; Levin, David I. W.; Jacobson, Alec; Mitra, Niloy and Viola, Ivan
We propose a novel algorithm to efficiently generate hidden structures to support arrangements of floating rigid objects. Our optimization finds a small set of rods and wires between objects and each other or a supporting surface (e.g., wall or ceiling) that hold all objects in force and torque equilibrium. Our objective function includes a sparsity inducing total volume term and a linear visibility term based on efficiently pre-computed Monte-Carlo integration, to encourage solutions that are as-hiddenas- possible. The resulting optimization is convex and the global optimum can be efficiently recovered via a linear program. Our representation allows for a user-controllable mixture of tension-, compression-, and shear-resistant rods or tension-only wires. We explore applications to theatre set design, museum exhibit curation, and other artistic endeavours.
Globally Injective Geometry Optimization with Non-Injective Steps
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Overby, Matthew; Kaufman, Danny; Narain, Rahul; Digne, Julie and Crane, Keenan
We present a method to minimize distortion and compute globally injective mappings from non-injective initialization. Many approaches for distortion minimization subject to injectivity constraints require an injective initialization and feasible intermediate states. However, it is often the case that injective initializers are not readily available, and many distortion energies of interest have barrier terms that stall global progress. The alternating direction method of multipliers (ADMM) has recently gained traction in graphics due to its efficiency and generality. In this work we explore how to endow ADMM with global injectivity while retaining the ability to traverse non-injective iterates. We develop an iterated coupled-solver approach that evolves two solution states in tandem. Our primary solver rapidly drives down energy to a nearly injective state using a dynamic set of efficiently enforceable inversion and overlap constraints. Then, a secondary solver corrects the state, herding the solution closer to feasibility. The resulting method not only compares well to previous work, but can also resolve overlap with free boundaries.
CommAID: Visual Analytics for Communication Analysis through Interactive Dynamics Modeling
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Fischer, Maximilian T.; Seebacher, Daniel; Sevastjanova, Rita; Keim, Daniel A.; El-Assady, Mennatallah; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Communication consists of both meta-information as well as content. Currently, the automated analysis of such data often focuses either on the network aspects via social network analysis or on the content, utilizing methods from text-mining. However, the first category of approaches does not leverage the rich content information, while the latter ignores the conversation environment and the temporal evolution, as evident in the meta-information. In contradiction to communication research, which stresses the importance of a holistic approach, both aspects are rarely applied simultaneously, and consequently, their combination has not yet received enough attention in automated analysis systems. In this work, we aim to address this challenge by discussing the difficulties and design decisions of such a path as well as contribute CommAID, a blueprint for a holistic strategy to communication analysis. It features an integrated visual analytics design to analyze communication networks through dynamics modeling, semantic pattern retrieval, and a user-adaptable and problem-specific machine learning-based retrieval system. An interactive multi-level matrix-based visualization facilitates a focused analysis of both network and content using inline visuals supporting cross-checks and reducing context switches. We evaluate our approach in both a case study and through formative evaluation with eight law enforcement experts using a real-world communication corpus. Results show that our solution surpasses existing techniques in terms of integration level and applicability. With this contribution, we aim to pave the path for a more holistic approach to communication analysis.
Exploring Multi-dimensional Data via Subset Embedding
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Xie, Peng; Tao, Wenyuan; Li, Jie; Huang, Wentao; Chen, Siming; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Multi-dimensional data exploration is a classic research topic in visualization. Most existing approaches are designed for identifying record patterns in dimensional space or subspace. In this paper, we propose a visual analytics approach to exploring subset patterns. The core of the approach is a subset embedding network (SEN) that represents a group of subsets as uniformlyformatted embeddings. We implement the SEN as multiple subnets with separate loss functions. The design enables to handle arbitrary subsets and capture the similarity of subsets on single features, thus achieving accurate pattern exploration, which in most cases is searching for subsets having similar values on few features. Moreover, each subnet is a fully-connected neural network with one hidden layer. The simple structure brings high training efficiency. We integrate the SEN into a visualization system that achieves a 3-step workflow. Specifically, analysts (1) partition the given dataset into subsets, (2) select portions in a projected latent space created using the SEN, and (3) determine the existence of patterns within selected subsets. Generally, the system combines visualizations, interactions, automatic methods, and quantitative measures to balance the exploration flexibility and operation efficiency, and improve the interpretability and faithfulness of the identified patterns. Case studies and quantitative experiments on multiple open datasets demonstrate the general applicability and effectiveness of our approach.
Orthogonalized Fourier Polynomials for Signal Approximation and Transfer
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Maggioli, Filippo; Melzi, Simone; Ovsjanikov, Maks; Bronstein, Michael M.; Rodolà, Emanuele; Mitra, Niloy and Viola, Ivan
We propose a novel approach for the approximation and transfer of signals across 3D shapes. The proposed solution is based on taking pointwise polynomials of the Fourier-like Laplacian eigenbasis, which provides a compact and expressive representation for general signals defined on the surface. Key to our approach is the construction of a new orthonormal basis upon the set of these linearly dependent polynomials. We analyze the properties of this representation, and further provide a complete analysis of the involved parameters. Our technique results in accurate approximation and transfer of various families of signals between near-isometric and non-isometric shapes, even under poor initialization. Our experiments, showcased on a selection of downstream tasks such as filtering and detail transfer, show that our method is more robust to discretization artifacts, deformation and noise as compared to alternative approaches.
A Multiscale Microfacet Model Based on Inverse Bin Mapping
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Atanasov, Asen; Wilkie, Alexander; Koylazov, Vladimir; Krivánek, Jaroslav; Mitra, Niloy and Viola, Ivan
Accurately controllable shading detail is a crucial aspect of realistic appearance modelling. Two fundamental building blocks for this are microfacet BRDFs, which describe the statistical behaviour of infinitely small facets, and normal maps, which provide user-controllable spatio-directional surface features. We analyse the filtering of the combined effect of a microfacet BRDF and a normal map. By partitioning the half-vector domain into bins we show that the filtering problem can be reduced to evaluation of an integral histogram (IH), a generalization of a summed-area table (SAT). Integral histograms are known for their large memory requirements, which are usually proportional to the number of bins. To alleviate this, we introduce Inverse Bin Maps, a specialised form of IH with a memory footprint that is practically independent of the number of bins. Based on these, we present a memory-efficient, production-ready approach for filtering of high resolution normal maps with arbitrary Beckmann flake roughness. In the corner case of specular normal maps (zero, or very small roughness values) our method shows similar convergence rates to the current state of the art, and is also more memory efficient.
Life cycle of SARS‐CoV‐2: from sketch to visualization in atomistic resolution
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Benes, Bedrich and Hauser, Helwig
SnakeBinning: Efficient Temporally Coherent Triangle Packing for Shading Streaming
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Hladky, Jozef; Seidel, Hans-Peter; Steinberger, Markus; Mitra, Niloy and Viola, Ivan
Streaming rendering, e.g., rendering in the cloud and streaming via a mobile connection, suffers from increased latency and unreliable connections. High quality framerate upsampling can hide these issues, especially when capturing shading into an atlas and transmitting it alongside geometric information. The captured shading information must consider triangle footprints and temporal stability to ensure efficient video encoding. Previous approaches only consider either temporal stability or sample distributions, but none focuses on both. With SnakeBinning, we present an efficient triangle packing approach that adjusts sample distributions and caters for temporal coherence. Using a multi-dimensional binning approach, we enforce tight packing among triangles while creating optimal sample distributions. Our binning is built on top of hardware supported real-time rendering where bins are mapped to individual pixels in a virtual framebuffer. Fragment shader interlock and atomic operations enforce global ordering of triangles within each bin, and thus temporal coherence according to the primitive order is achieved. Resampling the bin distribution guarantees high occupancy among all bins and a dense atlas packing. Shading samples are directly captured into the atlas using a rasterization pass, adjusting samples for perspective effects and creating a tight packing. Comparison to previous atlas packing approaches shows that our approach is faster than previous work and achieves the best sample distributions while maintaining temporal coherence. In this way, SnakeBinning achieves the highest rendering quality under equal atlas memory requirements. At the same time, its temporal coherence ensures that we require equal or less bandwidth than previous state-of-the-art. As SnakeBinning outperforms previous approach in all relevant aspects, it is the preferred choice for texture-based streaming rendering.
Functionality‐Driven Musculature Retargeting
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Ryu, Hoseok; Kim, Minseok; Lee, Seungwhan; Park, Moon Seok; Lee, Kyoungmin; Lee, Jehee; Benes, Bedrich and Hauser, Helwig
We present a novel retargeting algorithm that transfers the musculature of a reference anatomical model to new bodies with different sizes, body proportions, muscle capability, and joint range of motion while preserving the functionality of the original musculature as closely as possible. The geometric configuration and physiological parameters of musculotendon units are estimated and optimized to adapt to new bodies. The range of motion around joints is estimated from a motion capture dataset and edited further for individual models. The retargeted model is simulation‐ready, so we can physically simulate muscle‐actuated motor skills with the model. Our system is capable of generating a wide variety of anatomical bodies that can be simulated to walk, run, jump and dance while maintaining balance under gravity. We will also demonstrate the construction of individualized musculoskeletal models from bi‐planar X‐ray images and medical examination.
Temporally Dense Exploration of Moving and Deforming Shapes
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Frey, S.; Benes, Bedrich and Hauser, Helwig
We present our approach for the dense visualization and temporal exploration of moving and deforming shapes from scientific experiments and simulations. Our image space representation is created by convolving a noise texture along shape contours (akin to LIC). Beyond indicating spatial structure via luminosity, we additionally use colour to depict time or classes of shapes via automatically customized maps. This representation summarizes temporal evolution, and provides the basis for interactive user navigation in the spatial and temporal domain in combination with traditional renderings. Our efficient implementation supports the quick and progressive generation of our representation in parallel as well as adaptive temporal splits to reduce overlap. We discuss and demonstrate the utility of our approach using 2D and 3D scalar fields from experiments and simulations.
Geodesic Distance Computation via Virtual Source Propagation
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Trettner, Philip; Bommes, David; Kobbelt, Leif; Digne, Julie and Crane, Keenan
We present a highly practical, efficient, and versatile approach for computing approximate geodesic distances. The method is designed to operate on triangle meshes and a set of point sources on the surface. We also show extensions for all kinds of geometric input including inconsistent triangle soups and point clouds, as well as other source types, such as lines. The algorithm is based on the propagation of virtual sources and hence easy to implement. We extensively evaluate our method on about 10000 meshes taken from the Thingi10k and the Tet Meshing in theWild data sets. Our approach clearly outperforms previous approximate methods in terms of runtime efficiency and accuracy. Through careful implementation and cache optimization, we achieve runtimes comparable to other elementary mesh operations (e.g. smoothing, curvature estimation) such that geodesic distances become a ''first-class citizen'' in the toolbox of geometric operations. Our method can be parallelized and we observe up to 6x speed-up on the CPU and 20x on the GPU. We present a number of mesh processing tasks easily implemented on the basis of fast geodesic distances. The source code of our method is provided as a C++ library under the MIT license.
Egocentric Network Exploration for Immersive Analytics
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Sorger, Johannes; Arleo, Alessio; Kán, Peter; Knecht, Wolfgang; Waldner, Manuela; Zhang, Fang-Lue and Eisemann, Elmar and Singh, Karan
To exploit the potential of immersive network analytics for engaging and effective exploration, we promote the metaphor of ''egocentrism'', where data depiction and interaction are adapted to the perspective of the user within a 3D network. Egocentrism has the potential to overcome some of the inherent downsides of virtual environments, e.g., visual clutter and cyber-sickness. To investigate the effect of this metaphor on immersive network exploration, we designed and evaluated interfaces of varying degrees of egocentrism. In a user study, we evaluated the effect of these interfaces on visual search tasks, efficiency of network traversal, spatial orientation, as well as cyber-sickness. Results show that a simple egocentric interface considerably improves visual search efficiency and navigation performance, yet does not decrease spatial orientation or increase cyber-sickness. An occlusion-free Ego-Bubble view of the neighborhood only marginally improves the user's performance. We tie our findings together in an open online tool for egocentric network exploration, providing actionable insights on the benefits of the egocentric network exploration metaphor
Parametric Skeletons with Reduced Soft‐Tissue Deformations
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Tapia, Javier; Romero, Cristian; Pérez, Jesús; Otaduy, Miguel A.; Benes, Bedrich and Hauser, Helwig
We present a method to augment parametric skeletal models with subspace soft‐tissue deformations. We combine the benefits of data‐driven skeletal models, i.e. accurate replication of contact‐free static deformations, with the benefits of pure physics‐based models, i.e. skin and skeletal reaction to contact and inertial motion with two‐way coupling. We succeed to do so in a highly efficient manner, thanks to a careful choice of reduced model for the subspace deformation. With our method, it is easy to design expressive reduced models with efficient yet accurate force computations, without the need for training deformation examples. We demonstrate the application of our method to parametric models of human bodies, SMPL, and hands, MANO, with interactive simulations of contact with nonlinear soft‐tissue deformation and skeletal response.>
Patch Erosion for Deformable Lapped Textures on 3D Fluids
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Gagnon, Jonathan; Guzmán, Julián E.; Mould, David; Paquette, Eric; Mitra, Niloy and Viola, Ivan
We propose an approach to synthesise a texture on an animated fluid free surface using a distortion metric combined with a feature map. Our approach is applied as a post-process to a fluid simulation. We advect deformable patches to move the texture along the fluid flow. The patches are covering the whole surface every frame of the animation in an overlapping fashion. Using lapped textures combined with deformable patches, we successfully remove blending artifact and rigid artifact seen in previous methods. We remain faithful to the texture exemplar by removing distorted patch texels using a patch erosion process. The patch erosion is based on a feature map provided together with the exemplar as inputs to our approach. The erosion favors removing texels toward the boundary of the patch as well as texels corresponding to more distorted regions of the patch. Where texels are removed leaving a gap on the surface, we add new patches below existing ones. The result is an animated texture following the velocity field of the fluid. We compared our results with recent work and our results show that our approach removes ghosting and temporal fading artifacts.
Deep Reflectance Scanning: Recovering Spatially‐varying Material Appearance from a Flash‐lit Video Sequence
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Ye, Wenjie; Dong, Yue; Peers, Pieter; Guo, Baining; Benes, Bedrich and Hauser, Helwig
In this paper we present a novel method for recovering high‐resolution spatially‐varying isotropic surface reflectance of a planar exemplar from a flash‐lit close‐up video sequence captured with a regular hand‐held mobile phone. We do not require careful calibration of the camera and lighting parameters, but instead compute a per‐pixel flow map using a deep neural network to align the input video frames. For each video frame, we also extract the reflectance parameters, and warp the neural reflectance features directly using the per‐pixel flow, and subsequently pool the warped features. Our method facilitates convenient hand‐held acquisition of spatially‐varying surface reflectance with commodity hardware by non‐expert users. Furthermore, our method enables aggregation of reflectance features from surface points visible in only a subset of the captured video frames, enabling the creation of high‐resolution reflectance maps that exceed the native camera resolution. We demonstrate and validate our method on a variety of synthetic and real‐world spatially‐varying materials.
Semantics-Guided Latent Space Exploration for Shape Generation
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Jahan, Tansin; Guan, Yanran; Kaick, Oliver van; Mitra, Niloy and Viola, Ivan
We introduce an approach to incorporate user guidance into shape generation approaches based on deep networks. Generative networks such as autoencoders and generative adversarial networks are trained to encode shapes into latent vectors, effectively learning a latent shape space that can be sampled for generating new shapes. Our main idea is to enable users to explore the shape space with the use of high-level semantic keywords. Specifically, the user inputs a set of keywords that describe the general attributes of the shape to be generated, e.g., ''four legs'' for a chair. Then, our method maps the keywords to a subspace of the latent space, where the subspace captures the shapes possessing the specified attributes. The user then explores only this subspace to search for shapes that satisfy the design goal, in a process similar to using a parametric shape model. Our exploratory approach allows users to model shapes at a high level without the need for advanced artistic skills, in contrast to existing methods that allow to guide the generation with sketching or partial modeling of a shape. Our technical contribution to enable this exploration-based approach is the introduction of a label regression neural network coupled with shape encoder/decoder networks. The label regression network takes the user-provided keywords and maps them to distributions in the latent space. We show that our method allows users to explore the shape space and generate a variety of shapes with selected high-level attributes.
A Deeper Understanding of Visualization-Text Interplay in Geographic Data-driven Stories
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Latif, Shahid; Chen, Siming; Beck, Fabian; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Data-driven stories comprise of visualizations and a textual narrative. The two representations coexist and complement each other. Although existing research has explored the design strategies and structure of such stories, it remains an open research question how the two representations play together on a detailed level and how they are linked with each other. In this paper, we aim at understanding the fine-grained interplay of text and visualizations in geographic data-driven stories. We focus on geographic content as it often includes complex spatiotemporal data presented as versatile visualizations and rich textual descriptions. We conduct a qualitative empirical study on 22 stories collected from a variety of news media outlets; 10 of the stories report the COVID-19 pandemic, the others cover diverse topics. We investigate the role of every sentence and visualization within the narrative to reveal how they reference each other and interact. Moreover, we explore the positioning and sequence of various parts of the narrative to find patterns that further consolidate the stories. Drawing from the findings, we discuss study implications with respect to best practices and possibilities to automate the report generation.
Geometric Construction of Auxetic Metamaterials
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Bonneau, Georges-Pierre; Hahmann, Stefanie; Marku, Johana; Mitra, Niloy and Viola, Ivan
This paper is devoted to a category of metamaterials called auxetics, identified by their negative Poisson's ratio. Our work consists in exploring geometrical strategies to generate irregular auxetic structures. More precisely we seek to reduce the Poisson's ratio n, by pruning an irregular network based solely on geometric criteria. We introduce a strategy combining a pure geometric pruning algorithm followed by a physics-based testing phase to determine the resulting Poisson's ratio of our structures. We propose an algorithm that generates sets of irregular auxetic networks. Our contributions include geometrical characterization of auxetic networks, development of a pruning strategy, generation of auxetic networks with low Poisson's ratio, as well as validation of our approach.We provide statistical validation of our approach on large sets of irregular networks, and we additionally laser-cut auxetic networks in sheets of rubber. The findings reported here show that it is possible to reduce the Poisson's ratio by geometric pruning, and that we can generate irregular auxetic networks at lower processing times than a physics-based approach.

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