39-Issue 2

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https://diglib.eg.org/handle/10.2312/2632881

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A Practical Method for Animating Anisotropic Elastoplastic Materials
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Schreck, Camille; Wojtan, Chris; Panozzo, Daniele and Assarsson, Ulf
This paper introduces a simple method for simulating highly anisotropic elastoplastic material behaviors like the dissolution of fibrous phenomena (splintering wood, shredding bales of hay) and materials composed of large numbers of irregularly-shaped bodies (piles of twigs, pencils, or cards). We introduce a simple transformation of the anisotropic problem into an equivalent isotropic one, and we solve this new ''fictitious'' isotropic problem using an existing simulator based on the material point method. Our approach results in minimal changes to existing simulators, and it allows us to re-use popular isotropic plasticity models like the Drucker-Prager yield criterion instead of inventing new anisotropic plasticity models for every phenomenon we wish to simulate.
Fast and Scalable Solvers for the Fluid Pressure Equations with Separating Solid Boundary Conditions
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Lai, Junyu; Chen, Yangang; Gu, Yu; Batty, Christopher; Wan, Justin W. L.; Panozzo, Daniele and Assarsson, Ulf
In this paper, we propose and evaluate fast, scalable approaches for solving the linear complementarity problems (LCP) arising from the fluid pressure equations with separating solid boundary conditions. Specifically, we present a policy iteration method, a penalty method, and a modified multigrid method, and demonstrate that each is able to properly handle the desired boundary conditions. Moreover, we compare our proposed methods against existing approaches and show that our solvers are more efficient and exhibit better scaling behavior; that is, the number of iterations required for convergence is essentially independent of grid resolution, and thus they are faster at larger grid resolutions. For example, on a 256 grid our multigrid method was 30 times faster than the prior multigrid method in the literature.
EUROGRAPHICS 2020: CGF 39-2 Frontmatter
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Assarsson, Ulf; Panozzo, Daniele; Panozzo, Daniele and Assarsson, Ulf
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Segment Tracing Using Local Lipschitz Bounds
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Galin, Eric; Guérin, Eric; Paris, Axel; Peytavie, Adrien; Panozzo, Daniele and Assarsson, Ulf
We introduce Segment Tracing, a new algorithm that accelerates the classical Sphere Tracing method for computing the intersection between a ray and an implicit surface. Our approach consists in computing the Lipschitz bound locally over a segment to improve the marching step computation and accelerate the overall process. We describe the computation of the Lipschitz bound for different operators and primitives. We demonstrate that our algorithm significantly reduces the number of field function queries compared to previous methods, without the need for additional accelerating data-structures. Our method can be applied to a vast variety of implicit models ranging from hierarchical procedural objects built from complex primitives, to simulation-generated implicit surfaces created from many particles.
Interactively Modifying Compressed Sparse Voxel Representations
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Careil, Victor; Billeter, Markus; Eisemann, Elmar; Panozzo, Daniele and Assarsson, Ulf
Voxels are a popular choice to encode complex geometry. Their regularity makes updates easy and enables random retrieval of values. The main limitation lies in the poor scaling with respect to resolution. Sparse voxel DAGs (Directed Acyclic Graphs) overcome this hurdle and offer high-resolution representations for real-time rendering but only handle static data. We introduce a novel data structure to enable interactive modifications of such compressed voxel geometry without requiring de- and recompression. Besides binary data to encode geometry, it also supports compressed attributes (e.g., color). We illustrate the usefulness of our representation via an interactive large-scale voxel editor (supporting carving, filling, copying, and painting).
Modeling and Estimation of Nonlinear Skin Mechanics for Animated Avatars
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Romero, Cristian; Otaduy, Miguel A.; Casas, Dan; Pérez, Jesús; Panozzo, Daniele and Assarsson, Ulf
Data-driven models of human avatars have shown very accurate representations of static poses with soft-tissue deformations. However they are not yet capable of precisely representing very nonlinear deformations and highly dynamic effects. Nonlinear skin mechanics are essential for a realistic depiction of animated avatars interacting with the environment, but controlling physics-only solutions often results in a very complex parameterization task. In this work, we propose a hybrid model in which the soft-tissue deformation of animated avatars is built as a combination of a data-driven statistical model, which kinematically drives the animation, an FEM mechanical simulation. Our key contribution is the definition of deformation mechanics in a reference pose space by inverse skinning of the statistical model. This way, we retain as much as possible of the accurate static data-driven deformation and use a custom anisotropic nonlinear material to accurately represent skin dynamics. Model parameters including the heterogeneous distribution of skin thickness and material properties are automatically optimized from 4D captures of humans showing soft-tissue deformations.
SoftSMPL: Data-driven Modeling of Nonlinear Soft-tissue Dynamics for Parametric Humans
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Santesteban, Igor; Garces, Elena; Otaduy, Miguel A.; Casas, Dan; Panozzo, Daniele and Assarsson, Ulf
We present SoftSMPL, a learning-based method to model realistic soft-tissue dynamics as a function of body shape and motion. Datasets to learn such task are scarce and expensive to generate, which makes training models prone to overfitting. At the core of our method there are three key contributions that enable us to model highly realistic dynamics and better generalization capabilities than state-of-the-art methods, while training on the same data. First, a novel motion descriptor that disentangles the standard pose representation by removing subject-specific features; second, a neural-network-based recurrent regressor that generalizes to unseen shapes and motions; and third, a highly efficient nonlinear deformation subspace capable of representing soft-tissue deformations of arbitrary shapes. We demonstrate qualitative and quantitative improvements over existing methods and, additionally, we show the robustness of our method on a variety of motion capture databases.
Neural Temporal Adaptive Sampling and Denoising
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Hasselgren, Jon; Munkberg, Jacob; Salvi, Marco; Patney, Anjul; Lefohn, Aaron; Panozzo, Daniele and Assarsson, Ulf
Despite recent advances in Monte Carlo path tracing at interactive rates, denoised image sequences generated with few samples per-pixel often yield temporally unstable results and loss of high-frequency details. We present a novel adaptive rendering method that increases temporal stability and image fidelity of low sample count path tracing by distributing samples via spatio-temporal joint optimization of sampling and denoising. Adding temporal optimization to the sample predictor enables it to learn spatio-temporal sampling strategies such as placing more samples in disoccluded regions, tracking specular highlights, etc; adding temporal feedback to the denoiser boosts the effective input sample count and increases temporal stability. The temporal approach also allows us to remove the initial uniform sampling step typically present in adaptive sampling algorithms. The sample predictor and denoiser are deep neural networks that we co-train end-to-end over multiple consecutive frames. Our approach is scalable, allowing trade-off between quality and performance, and runs at near real-time rates while achieving significantly better image quality and temporal stability than previous methods.
Locally Supported Tangential Vector, n-Vector, and Tensor Fields
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Nasikun, Ahmad; Brandt, Christopher; Hildebrandt, Klaus; Panozzo, Daniele and Assarsson, Ulf
We introduce a construction of subspaces of the spaces of tangential vector, n-vector, and tensor fields on surfaces. The resulting subspaces can be used as the basis of fast approximation algorithms for design and processing problems that involve tangential fields. Important features of our construction are that it is based on a general principle, from which constructions for different types of tangential fields can be derived, and that it is scalable, making it possible to efficiently compute and store large subspace bases for large meshes. Moreover, the construction is adaptive, which allows for controlling the distribution of the degrees of freedom of the subspaces over the surface. We evaluate our construction in several experiments addressing approximation quality, scalability, adaptivity, computation times and memory requirements. Our design choices are justified by comparing our construction to possible alternatives. Finally, we discuss examples of how subspace methods can be used to build interactive tools for tangential field design and processing tasks.
Spectral Mollification for Bidirectional Fluorescence
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Jung, Alisa; Hanika, Johannes; Dachsbacher, Carsten; Panozzo, Daniele and Assarsson, Ulf
Fluorescent materials can shift energy between wavelengths, thereby creating bright and saturated colors both in natural and artificial materials. However, rendering fluorescence for continuous wavelengths or combined with wavelength dependent path configurations so far has only been feasible using spectral unidirectional methods. We present a regularization-based approach for supporting fluorescence in a spectral bidirectional path tracer. Our algorithm samples camera and light sub-paths with independent wavelengths, and when connecting them mollifies the BSDF at one of the connecting vertices such that it reradiates light across multiple wavelengths. We discuss arising issues such as color bias in early iterations, consistency of the method and MIS weights in the presence of spectral mollification. We demonstrate our method in scenes combining fluorescence and transport phenomena that are difficult to render with unidirectional or spectrally discrete methods.
Motion Retargetting based on Dilated Convolutions and Skeleton-specific Loss Functions
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Kim, SangBin; Park, Inbum; Kwon, Seongsu; Han, JungHyun; Panozzo, Daniele and Assarsson, Ulf
Motion retargetting refers to the process of adapting the motion of a source character to a target. This paper presents a motion retargetting model based on temporal dilated convolutions. In an unsupervised manner, the model generates realistic motions for various humanoid characters. The retargetted motions not only preserve the high-frequency detail of the input motions but also produce natural and stable trajectories despite the skeleton size differences between the source and target. Extensive experiments are made using a 3D character motion dataset and a motion capture dataset. Both qualitative and quantitative comparisons against prior methods demonstrate the effectiveness and robustness of our method.
Greedy Cut Construction for Parameterizations
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Zhu, Tianyu; Ye, Chunyang; Chai, Shuangming; Fu, Xiao-Ming; Panozzo, Daniele and Assarsson, Ulf
We present a novel method to construct short cuts for parameterizations with low isometric distortion. The algorithm contains two steps: (i) detect feature points, where the distortion is usually concentrated; and (ii) construct a cut by connecting the detected feature points. Central to each step is a greedy method. After generating a redundant feature point set, a greedy filtering process is performed to identify the feature points required for low isometric distortion parameterizations. This filtering process discards the feature points that are useless for distortion reduction while still enabling us to obtain low isometric distortion. Next, we formulate the process of connecting the detected feature points as a Steiner tree problem. To find an approximate solution, we first successively and greedily produce a collection of auxiliary points. Then, a cut is constructed by connecting the feature points and auxiliary points. In the 26,299 test cases in which an exact solution to the Steiner tree problem is available, the length of the cut obtained by our method is on average 0.17% longer than optimal. Compared to state-of-the-art cut construction methods, our method is one order of magnitude faster and generates shorter cuts while achieving similar isometric distortion.
Spherical Gaussian Light-field Textures for Fast Precomputed Global Illumination
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Currius, Roc Ramon; Dolonius, Dan; Assarsson, Ulf; Sintorn, Erik; Panozzo, Daniele and Assarsson, Ulf
We describe a method to use Spherical Gaussians with free directions and arbitrary sharpness and amplitude to approximate the precomputed local light field for any point on a surface in a scene. This allows for a high-quality reconstruction of these light fields in a manner that can be used to render the surfaces with precomputed global illumination in real-time with very low cost both in memory and performance. We also extend this concept to represent the illumination-weighted environment visibility, allowing for high-quality reflections of the distant environment with both surface-material properties and visibility taken into account. We treat obtaining the Spherical Gaussians as an optimization problem for which we train a Convolutional Neural Network to produce appropriate values for each of the Spherical Gaussians' parameters. We define this CNN in such a way that the produced parameters can be interpolated between adjacent local light fields while keeping the illumination in the intermediate points coherent
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).
Subdivision-Specialized Linear Algebra Kernels for Static and Dynamic Mesh Connectivity on the GPU
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Mlakar, Daniel; Winter, Martin; Stadlbauer, Pascal; Seidel, Hans-Peter; Steinberger, Markus; Zayer, Rhaleb; Panozzo, Daniele and Assarsson, Ulf
Subdivision surfaces have become an invaluable asset in production environments. While progress over the last years has allowed the use of graphics hardware to meet performance demands during animation and rendering, high-performance is limited to immutable mesh connectivity scenarios. Motivated by recent progress in mesh data structures, we show how the complete Catmull-Clark subdivision scheme can be abstracted in the language of linear algebra. While this high-level formulation allows for a fully parallel implementation with significant performance gains, the underlying algebraic operations require further specialization for modern parallel hardware. Integrating domain knowledge about the mesh matrix data structure, we replace costly general linear algebra operations like matrix-matrix multiplication by specialized kernels. By further considering innate properties of Catmull-Clark subdivision, like the quad-only structure after refinement, we achieve an additional order of magnitude in performance and significantly reduce memory footprints. Our approach can be adapted seamlessly for different use cases, such as regular subdivision of dynamic meshes, fast evaluation for immutable topology and feature-adaptive subdivision for efficient rendering of animated models. In this way, patchwork solutions are avoided in favor of a streamlined solution with consistent performance gains throughout the production pipeline. The versatility of the sparse matrix linear algebra abstraction underlying our work is further demonstrated by extension to other schemes such as √3 and Loop subdivision.
Expression Packing: As-Few-As-Possible Training Expressions for Blendshape Transfer
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Carrigan, Emma; Zell, Eduard; Guiard, Cedric; McDonnell, Rachel; Panozzo, Daniele and Assarsson, Ulf
To simplify and accelerate the creation of blendshape rigs, using a template rig is a common procedure, especially during the creation of digital doubles. Blendshape transfer methods facilitate copy and paste functionality of the blendshapes from the template model to the digital double. However, for adequate personalization, such methods require a set of scanned training expressions of the original actor. So far, the semantics of the facial expressions to scan have been defined manually. In contrast, we formulate the semantics of the facial expressions as an integer optimization of the blendshape weights. By combining different blendshapes of the template model, our method creates facial expressions that serve as semantic references during scanning. Our method guarantees to compute as-few-as-possible training expressions with minimal overlap of activated blendshapes. If the number of training expressions is limited, blendshapes are selected based on their power to personalize the resulting blendshapes compared to generic blendshape transfer methods.
Polygon Laplacian Made Simple
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Bunge, Astrid; Herholz, Philipp; Kazhdan, Misha; Botsch, Mario; Panozzo, Daniele and Assarsson, Ulf
The discrete Laplace-Beltrami operator for surface meshes is a fundamental building block for many (if not most) geometry processing algorithms. While Laplacians on triangle meshes have been researched intensively, yielding the cotangent discretization as the de-facto standard, the case of general polygon meshes has received much less attention. We present a discretization of the Laplace operator which is consistent with its expression as the composition of divergence and gradient operators, and is applicable to general polygon meshes, including meshes with non-convex, and even non-planar, faces. By virtually inserting a carefully placed point we implicitly refine each polygon into a triangle fan, but then hide the refinement within the matrix assembly. The resulting operator generalizes the cotangent Laplacian, inherits its advantages, and is empirically shown to be on par or even better than the recent polygon Laplacian of Alexa and Wardetzky [AW11] - while being simpler to compute.
Binary Ostensibly-Implicit Trees for Fast Collision Detection
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Chitalu, Floyd M.; Dubach, Christophe; Komura, Taku; Panozzo, Daniele and Assarsson, Ulf
We present a simple, efficient and low-memory technique, targeting fast construction of bounding volume hierarchies (BVH) for broad-phase collision detection. To achieve this, we devise a novel representation of BVH trees in memory. We develop a mapping of the implicit index representation to compact memory locations, based on simple bit-shifts, to then construct and evaluate bounding volume test trees (BVTT) during collision detection with real-time performance. We model the topology of the BVH tree implicitly as binary encodings which allows us to determine the nodes missing from a complete binary tree using the binary representation of the number of missing nodes. The simplicity of our technique allows for fast hierarchy construction achieving over 6x speedup over the state-of-the-art. Making use of these characteristics, we show that not only it is feasible to rebuild the BVH at every frame, but that using our technique, it is actually faster than refitting and more memory efficient.
Persistence Analysis of Multi-scale Planar Structure Graph in Point Clouds
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Lejemble, Thibault; Mura, Claudio; Barthe, Loïc; Mellado, Nicolas; Panozzo, Daniele and Assarsson, Ulf
Modern acquisition techniques generate detailed point clouds that sample complex geometries. For instance, we are able to produce millimeter-scale acquisition of whole buildings. Processing and exploring geometrical information within such point clouds requires scalability, robustness to acquisition defects and the ability to model shapes at different scales. In this work, we propose a new representation that enriches point clouds with a multi-scale planar structure graph. We define the graph nodes as regions computed with planar segmentations at increasing scales and the graph edges connect regions that are similar across scales. Connected components of the graph define the planar structures present in the point cloud within a scale interval. For instance, with this information, any point is associated to one or several planar structures existing at different scales. We then use topological data analysis to filter the graph and provide the most prominent planar structures. Our representation naturally encodes a large range of information. We show how to efficiently extract geometrical details (e.g. tiles of a roof), arrangements of simple shapes (e.g. steps and mean ramp of a staircase), and large-scale planar proxies (e.g. walls of a building) and present several interactive tools to visualize, select and reconstruct planar primitives directly from raw point clouds. The effectiveness of our approach is demonstrated by an extensive evaluation on a variety of input data, as well as by comparing against state-of-the-art techniques and by showing applications to polygonal mesh reconstruction.
Robust Shape Collection Matching and Correspondence from Shape Differences
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Cohen, Aharon; Ben-Chen, Mirela; Panozzo, Daniele and Assarsson, Ulf
We propose a method to automatically match two shape collections with a similar shape space structure, e.g. two characters in similar poses, and compute the inter-maps between the collections. Given the intra-maps in each collection, we extract the corresponding shape difference operators, and use them to construct an embedding of the shape space of each collection. We then align the two shape spaces, and use the knowledge gained from the alignment to compute the inter-maps. Unlike existing approaches for collection alignment, our method is applicable to small and large collections alike, and requires no parameter tuning. Furthermore, unlike most approaches for non-isometric correspondence, our method uses solely the variation within the collection to extract the inter-maps, and therefore does not require landmarks, descriptors or any additional input. We demonstrate that we achieve high matching accuracy rates, and compute high quality maps on non-isometric shapes, which compare favorably with automatic state-of-the-art methods for non-isometric shape correspondence.

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