39-Issue 2

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

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Fast and Robust Stochastic Structural Optimization
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Cui, Qiaodong; Langlois, Timothy; Sen, Pradeep; Kim, Theodore; Panozzo, Daniele and Assarsson, Ulf
Stochastic structural analysis can assess whether a fabricated object will break under real-world conditions. While this approach is powerful, it is also quite slow, which has previously limited its use to coarse resolutions (e.g., 26x34x28). We show that this approach can be made asymptotically faster, which in practice reduces computation time by two orders of magnitude, and allows the use of previously-infeasible resolutions. We achieve this by showing that the probability gradient can be computed in linear time instead of quadratic, and by using a robust new scheme that stabilizes the inertia gradients used by the optimization. Additionally, we propose a constrained restart method that deals with local minima, and a sheathing approach that further reduces the weight of the shape. Together, these components enable the discovery of previously-inaccessible designs.
Computational Design and Optimization of Non-Circular Gears
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Xu, Hao; Fu, Tianwen; Song, Peng; Zhou, Mingjun; Fu, Chi-Wing; Mitra, Niloy J.; Panozzo, Daniele and Assarsson, Ulf
We study a general form of gears known as non-circular gears that can transfer periodic motion with variable speed through their irregular shapes and eccentric rotation centers. To design functional non-circular gears is nontrivial, since the gear pair must have compatible shape to keep in contact during motion, so the driver gear can push the follower to rotate via a bounded torque that the motor can exert. To address the challenge, we model the geometry, kinematics, and dynamics of non-circular gears, formulate the design problem as a shape optimization, and identify necessary independent variables in the optimization search. Taking a pair of 2D shapes as inputs, our method optimizes them into gears by locating the rotation center on each shape, minimally modifying each shape to form the gear's boundary, and constructing appropriate teeth for gear meshing. Our optimized gears not only resemble the inputs but can also drive the motion with relatively small torque. We demonstrate our method's usability by generating a rich variety of non-circular gears from various inputs and 3D printing several of them.
Fast and Robust QEF Minimization using Probabilistic Quadrics
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Trettner, Philip; Kobbelt, Leif; Panozzo, Daniele and Assarsson, Ulf
Error quadrics are a fundamental and powerful building block in many geometry processing algorithms. However, finding the minimizer of a given quadric is in many cases not robust and requires a singular value decomposition or some ad-hoc regularization. While classical error quadrics measure the squared deviation from a set of ground truth planes or polygons, we treat the input data as genuinely uncertain information and embed error quadrics in a probabilistic setting (''probabilistic quadrics'') where the optimal point minimizes the expected squared error. We derive closed form solutions for the popular plane and triangle quadrics subject to (spatially varying, anisotropic) Gaussian noise. Probabilistic quadrics can be minimized robustly by solving a simple linear system-50x faster than SVD. We show that probabilistic quadrics have superior properties in tasks like decimation and isosurface extraction since they favor more uniform triangulations and are more tolerant to noise while still maintaining feature sensitivity. A broad spectrum of applications can directly benefit from our new quadrics as a drop-in replacement which we demonstrate with mesh smoothing via filtered quadrics and non-linear subdivision surfaces.
Designing Robotically-Constructed Metal Frame Structures
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Ma, Zhao; Walzer, Alexander; Schumacher, Christian; Rust, Romana; Gramazio, Fabio; Kohler, Matthias; Bächer, Moritz; Panozzo, Daniele and Assarsson, Ulf
We present a computational technique that aids with the design of structurally-sound metal frames, tailored for robotic fabrication using an existing process that integrate automated bar bending, welding, and cutting. Aligning frames with structurallyfavorable orientations, and decomposing models into fabricable units, we make the fabrication process scale-invariant, and frames globally align in an aesthetically-pleasing and structurally-informed manner. Relying on standard analysis of frames, we then co-optimize the shape and topology of bars at the local unit level. At this level, we minimize combinations of functional and aesthetic objectives under strict fabrication constraints that model the assembly of discrete sets of bent bars. We demonstrate the capabilities of our global-to-local approach on four robotically-constructed examples.
Combinatorial Construction of Seamless Parameter Domains
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Zhou, Jiaran; Tu, Changhe; Zorin, Denis; Campen, Marcel; Panozzo, Daniele and Assarsson, Ulf
The problem of seamless parametrization of surfaces is of interest in the context of structured quadrilateral mesh generation and spline-based surface approximation. It has been tackled by a variety of approaches, commonly relying on continuous numerical optimization to ultimately obtain suitable parameter domains. We present a general combinatorial seamless parameter domain construction, free from the potential numerical issues inherent to continuous optimization techniques in practice. The domains are constructed as abstract polygonal complexes which can be embedded in a discrete planar grid space, as unions of unit squares. We ensure that the domain structure matches any prescribed parametrization singularities (cones) and satisfies seamlessness conditions. Surfaces of arbitrary genus are supported. Once a domain suitable for a given surface is constructed, a seamless and locally injective parametrization over this domain can be obtained using existing planar disk mapping techniques, making recourse to Tutte's classical embedding theorem.
An Efficient Transport Estimator for Complex Layered Materials
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Gamboa, Luis E.; Gruson, Adrien; Nowrouzezahrai, Derek; Panozzo, Daniele and Assarsson, Ulf
Layered materials capture subtle, realistic reflection behaviors that traditional single-layer models lack. Much of this is due to the complex subsurface light transport at the interfaces of - and in the media between - layers. Rendering with these materials can be costly, since we must simulate these transport effects at every evaluation of the underlying reflectance model. Rendering an image requires thousands of such evaluations, per pixel. Recent work treats this complexity by introducing significant approximations, requiring large precomputed datasets per material, or simplifying the light transport simulations within the materials. Even the most effective of these methods struggle with the complexity induced by high-frequency variation in reflectance parameters and micro-surface normal variation, as well as anisotropic volumetric scattering between the layer interfaces. We present a more efficient, unbiased estimator for light transport in such general, complex layered appearance models. By conducting an analysis of the types of transport paths that contribute most to the aggregate reflectance dynamics, we propose an effective and unbiased path sampling method that reduces variance in the reflectance evaluations. Our method additionally supports reflectance importance sampling, does not rely on any precomputation, and so integrates readily into existing renderers. We consistently outperform the state-of-the-art by ~2-6x in equal-quality (i.e., equal error) comparisons.
Simulation of Dendritic Painting
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Canabal, José A.; Otaduy, Miguel A.; Kim, Byungmoon; Echevarria, Jose; Panozzo, Daniele and Assarsson, Ulf
We present a new system for interactive dendritic painting. Dendritic painting is characterized by the unique and intricate branching patterns that grow from the interaction of inks, solvents and medium. Painting sessions thus become very dynamic and experimental. To achieve a compelling simulation of this painting technique we introduce a new Reaction-Diffusion model with carefully designed terms to allow natural interactions in a painting context. We include additional user control not possible in the real world to guide and constrain the growth of the patterns in expressive ways. Our multi-field model is able to capture and simulate all these complex phenomena efficiently in real time, expanding the tools available to the digital artist, while producing compelling animations for motion graphics.
Stratified Markov Chain Monte Carlo Light Transport
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Gruson, Adrien; West, Rex; Hachisuka, Toshiya; Panozzo, Daniele and Assarsson, Ulf
Markov chain Monte Carlo (MCMC) sampling is a powerful approach to generate samples from an arbitrary distribution. The application to light transport simulation allows us to efficiently handle complex light transport such as highly occluded scenes. Since light transport paths in MCMC methods are sampled according to the path contributions over the sampling domain covering the whole image, bright pixels receive more samples than dark pixels to represent differences in the brightness. This variation in the number of samples per pixel is a fundamental property of MCMC methods. This property often leads to uneven convergence over the image, which is a notorious and fundamental issue of any MCMC method to date. We present a novel stratification method of MCMC light transport methods. Our stratification method, for the first time, breaks the fundamental limitation that the number of samples per pixel is uncontrollable. Our method guarantees that every pixel receives a specified number of samples by running a single Markov chain per pixel. We rely on the fact that different MCMC processes should converge to the same result when the sampling domain and the integrand are the same. We thus subdivide an image into multiple overlapping tiles associated with each pixel, run an independent MCMC process in each of them, and then align all of the tiles such that overlapping regions match. This can be formulated as an optimization problem similar to the reconstruction step for gradient-domain rendering. Further, our method can exploit the coherency of integrands among neighboring pixels via coherent Markov chains and replica exchange. Images rendered with our method exhibit much more predictable convergence compared to existing MCMC methods.
Facial Expression Synthesis using a Global-Local Multilinear Framework
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Wang, Mengjiao; Bradley, Derek; Zafeiriou, Stefanos; Beeler, Thabo; Panozzo, Daniele and Assarsson, Ulf
We present a practical method to synthesize plausible 3D facial expressions for a particular target subject. The ability to synthesize an entire facial rig from a single neutral expression has a large range of applications both in computer graphics and computer vision, ranging from the efficient and cost-effective creation of CG characters to scalable data generation for machine learning purposes. Unlike previous methods based on multilinear models, the proposed approach is capable to extrapolate well outside the sample pool, which allows it to plausibly predict the identity of the target subject and create artifact free expression shapes while requiring only a small input dataset. We introduce global-local multilinear models that leverage the strengths of expression-specific and identity-specific local models combined with coarse motion estimations from a global model. Experimental results show that we achieve high-quality, plausible facial expression synthesis results for an individual that outperform existing methods both quantitatively and qualitatively.
Local Bases for Model-reduced Smoke Simulations
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Mercier, Olivier; Nowrouzezahrai, Derek; Panozzo, Daniele and Assarsson, Ulf
We present a flexible model reduction method for simulating incompressible fluids.We derive a novel vector field basis composed of localized basis flows which have simple analytic forms and can be tiled on regular lattices, avoiding the use of complicated data structures or neighborhood queries. Local basis flow interactions can be precomputed and reused to simulate fluid dynamics on any simulation domain without additional overhead. We introduce heuristic simulation dynamics tailored to our basis and derived from a projection of the Navier-Stokes equations to produce physically plausible motion, exposing intuitive parameters to control energy distribution across scales. Our basis can adapt to curved simulation boundaries, can be coupled with dynamic obstacles, and offers simple adjustable trade-offs between speed and visual resolution.
Unified Neural Encoding of BTFs
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Rainer, Gilles; Ghosh, Abhijeet; Jakob, Wenzel; Weyrich, Tim; Panozzo, Daniele and Assarsson, Ulf
Realistic rendering using discrete reflectance measurements is challenging, because arbitrary directions on the light and view hemispheres are queried at render time, incurring large memory requirements and the need for interpolation. This explains the desire for compact and continuously parametrized models akin to analytic BRDFs; however, fitting BRDF parameters to complex data such as BTF texels can prove challenging, as models tend to describe restricted function spaces that cannot encompass real-world behavior. Recent advances in this area have increasingly relied on neural representations that are trained to reproduce acquired reflectance data. The associated training process is extremely costly and must typically be repeated for each material. Inspired by autoencoders, we propose a unified network architecture that is trained on a variety of materials, and which projects reflectance measurements to a shared latent parameter space. Similarly to SVBRDF fitting, real-world materials are represented by parameter maps, and the decoder network is analog to the analytic BRDF expression (also parametrized on light and view directions for practical rendering application). With this approach, encoding and decoding materials becomes a simple matter of evaluating the network. We train and validate on BTF datasets of the University of Bonn, but there are no prerequisites on either the number of angular reflectance samples, or the sample positions. Additionally, we show that the latent space is well-behaved and can be sampled from, for applications such as mipmapping and texture synthesis.
Mixing Yarns and Triangles in Cloth Simulation
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Casafranca, Juan J.; Cirio, Gabriel; Rodríguez, Alejandro; Miguel, Eder; Otaduy, Miguel A.; Panozzo, Daniele and Assarsson, Ulf
This paper presents a method to combine triangle and yarn models in cloth simulation, and hence leverage their best features. The majority of a garment uses a triangle-based model, which reduces the overall computational and memory cost. Key areas of the garment use a yarn-based model, which elicits rich effects such as structural nonlinearity and plasticity. To combine both models in a seamless and robust manner, we solve two major technical challenges. We propose an enriched kinematic representation that augments triangle-based deformations with yarn-level details. Naïve enrichment suffers from kinematic redundancy, but we devise an optimal kinematic filter that allows a smooth transition between triangle and yarn models. We also introduce a preconditioner that resolves the poor conditioning produced by the extremely different inertia of triangle and yarn nodes. This preconditioner deals effectively with rank deficiency introduced by the kinematic filter. We demonstrate that mixed yarns and triangles succeed to efficiently capture rich effects in garment fit and drape.
Optimizing Object Decomposition to Reduce Visual Artifacts in 3D Printing
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Filoscia, Irene; Alderighi, Thomas; Giorgi, Daniela; Malomo, Luigi; Callieri, Marco; Cignoni, Paolo; Panozzo, Daniele and Assarsson, Ulf
We propose a method for the automatic segmentation of 3D objects into parts which can be individually 3D printed and then reassembled by preserving the visual quality of the final object. Our technique focuses on minimizing the surface affected by supports, decomposing the object into multiple parts whose printing orientation is automatically chosen. The segmentation reduces the visual impact on the fabricated model producing non-planar cuts that adapt to the object shape. This is performed by solving an optimization problem that balances the effects of supports and cuts, while trying to place both in occluded regions of the object surface. To assess the practical impact of the solution, we show a number of segmented, 3D printed and reassembled objects.
Displacement-Correlated XFEM for Simulating Brittle Fracture
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Chitalu, Floyd M.; Miao, Qinghai; Subr, Kartic; Komura, Taku; Panozzo, Daniele and Assarsson, Ulf
We present a remeshing-free brittle fracture simulation method under the assumption of quasi-static linear elastic fracture mechanics (LEFM). To achieve this, we devise two algorithms. First, we develop an approximate volumetric simulation, based on the extended Finite Element Method (XFEM), to initialize and propagate Lagrangian crack-fronts. We model the geometry of fracture explicitly as a surface mesh, which allows us to generate high-resolution crack surfaces that are decoupled from the resolution of the deformation mesh. Our second contribution is a mesh cutting algorithm, which produces fragments of the input mesh using the fracture surface. We do this by directly operating on the half-edge data structures of two surface meshes, which enables us to cut general surface meshes including those of concave polyhedra and meshes with abutting concave polygons. Since we avoid triangulation for cutting, the connectivity of the resulting fragments is identical to the (uncut) input mesh except at edges introduced by the cut. We evaluate our simulation and cutting algorithms and show that they outperform state-of-the-art approaches both qualitatively and quantitatively.
Interactive Meso-scale Simulation of Skyscapes
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Vimont, Ulysse; Gain, James; Lastic, Maud; Cordonnier, Guillaume; Abiodun, Babatunde; Cani, Marie-Paule; Panozzo, Daniele and Assarsson, Ulf
Although an important component of natural scenes, the representation of skyscapes is often relatively simplistic. This can be largely attributed to the complexity of the thermodynamics underpinning cloud evolution and wind dynamics, which make interactive simulation challenging.We address this problem by introducing a novel layered model that encompasses both terrain and atmosphere, and supports efficient meteorological simulations. The vertical and horizontal layer resolutions can be tuned independently, while maintaining crucial inter-layer thermodynamics, such as convective circulation and land-air transfers of heat and moisture. In addition, we introduce a cloud-form taxonomy for clustering, classifying and upsampling simulation cells to enable visually plausible, finely-sampled volumetric rendering. As our results demonstrate, this pipeline allows interactive simulation followed by up-sampled rendering of extensive skyscapes with dynamic clouds driven by consistent wind patterns. We validate our method by reproducing characteristic phenomena such as diurnal shore breezes, convective cells that contribute to cumulus cloud formation, and orographic effects from moist air driven upslope.
Style-Controllable Speech-Driven Gesture Synthesis Using Normalising Flows
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Alexanderson, Simon; Henter, Gustav Eje; Kucherenko, Taras; Beskow, Jonas; Panozzo, Daniele and Assarsson, Ulf
Automatic synthesis of realistic gestures promises to transform the fields of animation, avatars and communicative agents. In off-line applications, novel tools can alter the role of an animator to that of a director, who provides only high-level input for the desired animation; a learned network then translates these instructions into an appropriate sequence of body poses. In interactive scenarios, systems for generating natural animations on the fly are key to achieving believable and relatable characters. In this paper we address some of the core issues towards these ends. By adapting a deep learning-based motion synthesis method called MoGlow, we propose a new generative model for generating state-of-the-art realistic speech-driven gesticulation. Owing to the probabilistic nature of the approach, our model can produce a battery of different, yet plausible, gestures given the same input speech signal. Just like humans, this gives a rich natural variation of motion. We additionally demonstrate the ability to exert directorial control over the output style, such as gesture level, speed, symmetry and spacial extent. Such control can be leveraged to convey a desired character personality or mood. We achieve all this without any manual annotation of the data. User studies evaluating upper-body gesticulation confirm that the generated motions are natural and well match the input speech. Our method scores above all prior systems and baselines on these measures, and comes close to the ratings of the original recorded motions. We furthermore find that we can accurately control gesticulation styles without unnecessarily compromising perceived naturalness. Finally, we also demonstrate an application of the same method to full-body gesticulation, including the synthesis of stepping motion and stance.
Interactive Modeling of Cellular Structures on Surfaces with Application to Additive Manufacturing
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Stadlbauer, Pascal; Mlakar, Daniel; Seidel, Hans-Peter; Steinberger, Markus; Zayer, Rhaleb; Panozzo, Daniele and Assarsson, Ulf
The rich and evocative patterns of natural tessellations endow them with an unmistakable artistic appeal and structural properties which are echoed across design, production, and manufacturing. Unfortunately, interactive control of such patterns-as modeled by Voronoi diagrams, is limited to the simple two dimensional case and does not extend well to freeform surfaces. We present an approach for direct modeling and editing of such cellular structures on surface meshes. The overall modeling experience is driven by a set of editing primitives which are efficiently implemented on graphics hardware. We feature a novel application for 3D printing on modern support-free additive manufacturing platforms. Our method decomposes the input surface into a cellular skeletal structure which hosts a set of overlay shells. In this way, material saving can be channeled to the shells while structural stability is channeled to the skeleton. To accommodate the available printer build volume, the cellular structure can be further split into moderately sized parts. Together with shells, they can be conveniently packed to save on production time. The assembly of the printed parts is streamlined by a part numbering scheme which respects the geometric layout of the input model.
Accurate Real-time 3D Gaze Tracking Using a Lightweight Eyeball Calibration
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Wen, Quan; Bradley, Derek; Beeler, Thabo; Park, Seonwook; Hilliges, Otmar; Yong, Jun-Hai; Xu, Feng; Panozzo, Daniele and Assarsson, Ulf
3D gaze tracking from a single RGB camera is very challenging due to the lack of information in determining the accurate gaze target from a monocular RGB sequence. The eyes tend to occupy only a small portion of the video, and even small errors in estimated eye orientations can lead to very large errors in the triangulated gaze target. We overcome these difficulties with a novel lightweight eyeball calibration scheme that determines the user-specific visual axis, eyeball size and position in the head. Unlike the previous calibration techniques, we do not need the ground truth positions of the gaze points. In the online stage, gaze is tracked by a new gaze fitting algorithm, and refined by a 3D gaze regression method to correct for bias errors. Our regression is pre-trained on several individuals and works well for novel users. After the lightweight one-time user calibration, our method operates in real time. Experiments show that our technique achieves state-of-the-art accuracy in gaze angle estimation, and we demonstrate applications of 3D gaze target tracking and gaze retargeting to an animated 3D character.
Progressive Real-Time Rendering of One Billion Points Without Hierarchical Acceleration Structures
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Schütz, Markus; Mandlburger, Gottfried; Otepka, Johannes; Wimmer, Michael; Panozzo, Daniele and Assarsson, Ulf
Research in rendering large point clouds traditionally focused on the generation and use of hierarchical acceleration structures that allow systems to load and render the smallest fraction of the data with the largest impact on the output. The generation of these structures is slow and time consuming, however, and therefore ill-suited for tasks such as quickly looking at scan data stored in widely used unstructured file formats, or to immediately display the results of point-cloud processing tasks. We propose a progressive method that is capable of rendering any point cloud that fits in GPU memory in real time, without the need to generate hierarchical acceleration structures in advance. Our method supports data sets with a large amount of attributes per point, achieves a load performance of up to 100 million points per second, displays already loaded data in real time while remaining data is still being loaded, and is capable of rendering up to one billion points using an on-the-fly generated shuffled vertex buffer as its data structure, instead of slow-to-generate hierarchical structures. Shuffling is done during loading in order to allow efficiently filling holes with random subsets, which leads to a higher quality convergence behavior.
RGB2AO: Ambient Occlusion Generation from RGB Images
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Inoue, Naoto; Ito, Daichi; Hold-Geoffroy, Yannick; Mai, Long; Price, Brian; Yamasaki, Toshihiko; Panozzo, Daniele and Assarsson, Ulf
We present RGB2AO, a novel task to generate ambient occlusion (AO) from a single RGB image instead of screen space buffers such as depth and normal. RGB2AO produces a new image filter that creates a non-directional shading effect that darkens enclosed and sheltered areas. RGB2AO aims to enhance two 2D image editing applications: image composition and geometryaware contrast enhancement. We first collect a synthetic dataset consisting of pairs of RGB images and AO maps. Subsequently, we propose a model for RGB2AO by supervised learning of a convolutional neural network (CNN), considering 3D geometry of the input image. Experimental results quantitatively and qualitatively demonstrate the effectiveness of our model.

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