We present a methodology to personalize the stiffness response of a biomechanical model of the torso and the spine. In high contrast to previous work, the proposed methodology uses controlled force–deformation data that mimic the conditions of spinal bracing for scoliosis, which leads to personalized biomechanical models that are suitable for computational brace design. The novel methodology relies on several technical contributions. First, a prototype system that includes controlled force measurement and low-dose radiographs, with low-encumbrance for its implementation in the clinical protocol. Second, a model of differentiable biomechanics of the torso and the spine, which becomes the key building block for robust parameter estimation. And third, an optimization procedure for parameter estimation from force–deformation data, which relies on differentiability of the biomechanics and the image generation process. We demonstrate the application of the methodology to a cohort of 7 subjects who underwent scoliosis check-ups, and we show quantitative validation of the estimated personalized parameters and the improvement over default parameters from the bibliography. • Personalized stiffness estimation of the spine and torso biomechanics. • Prototype for measurement of force–deformation data of the spine. • Differentiable biomechanics model of the torso and spine. • Image-based optimization algorithm for parameter estimation.

We propose the formulation of forward and differentiable rigid-body dynamics using Lie-algebra rotation derivatives. In particular, we show how this approach can easily be applied to incremental-potential formulations of forward dymamics, and we introduce a novel definition of adjoints for differentiable dynamics. In contrast to other parameterizations of rotations (notably the popular rotation-vector parameterization), our approach leads to painlessly simple and compact derivatives, better conditioning, and higher runtime efficiency. We demonstrate our approach on fundamental rigid-body problems, but also on Cosserat rods as an example of multi-rigid-body dynamics.

This paper introduces a new formulation for material homogenization of thin-shell microstructures. It addresses important challenges that limit the quality of previous approaches: methods that fit the energy response neglect visual impact, methods that fit the stress response are not conservative, and all of them are limited to a low-dimensional interplay between deformation modes. The new formulation is rooted on the following design principles: the material energy functions are conservative by definition, they are formulated on the high-dimensional membrane and bending domain to capture the complex interplay of the different deformation modes, the material function domain is maximally aligned with the training data, and the material parameters and the optimization are formulated on stress instead of energy for better correlation with visual impact. The key novelty of our formulation is a new type of high-order RBF interpolant for polar coordinates, which allows us to fulfill all the design principles. We design a material function using this novel interpolant, as well as an overall homogenization workflow. Our results demonstrate very accurate fitting of diverse microstructure behaviors, both quantitatively and qualitatively superior to previous work.

We propose a novel contact-aware method to synthesize highly-dense 3D crowds of animated characters. Existing methods animate crowds by, first, computing the 2D global motion approximating subjects as 2D particles and, then, introducing individual character motions without considering their surroundings. This creates the illusion of a 3D crowd, but, with density, characters frequently intersect each other since character-to-character contact is not modeled. We tackle this issue and propose a general method that considers any crowd animation and resolves existing residual collisions. To this end, we take a physics-based approach to model contacts between articulated characters. This enables the real-time synthesis of 3D high-density crowds with dozens of individuals that do not intersect each other, producing an unprecedented level of physical correctness in animations. Under the hood, we model each individual using a parametric human body incorporating a set of 3D proxies to approximate their volume. We then build a large system of articulated rigid bodies, and use an efficient physics-based approach to solve for individual body poses that do not collide with each other while maintaining the overall motion of the crowd. We first validate our approach objectively and quantitatively. We then explore relations between physical correctness and perceived realism based on an extensive user study that evaluates the relevance of solving contacts in dense crowds. Results demonstrate that our approach outperforms existing methods for crowd animation in terms of geometric accuracy and overall realism.

We present a multi-person soft-tissue avatar model. This model maps a body shape descriptor to heterogeneous geometric and mechanical parameters of a soft-tissue model across the body, effectively producing a shape-dependent parametric soft avatar model. The design of the model overcomes two major challenges, the potential redundancy of geometric and mechanical parameters, and the complexity to obtain abundant subject data, which together induce major risk of overfitting the resulting model. To overcome these challenges, we introduce a local shape-dependent regularization of the model. We demonstrate accurate results, on par with independent per-subject estimation, accurate interpolation within the range of body shapes of the training subjects, and good generalization to unseen body shapes. As a result, we obtain a parametric soft-flesh avatar model easy to integrate in many existing applications.

This paper presents a learning-based method for the simulation of rich contact deformations on reduced deformation models. Previous works learn deformation models for specific pairs of objects; we lift this limitation by designing a neural model that supports general rigid collider shapes. We do this by formulating a novel collider descriptor that characterizes local geometry in a region of interest. The paper shows that the learning-based deformation model can be trained on a library of colliders, but it accurately supports unseen collider shapes at runtime. We showcase our method on interactive dynamic simulations with animation of rich deformation detail, manipulation and exploration of untrained objects, and augmentation of contact information suitable for high-fidelity haptics.

We present a self-supervised method to learn dynamic 3D deformations of garments worn by parametric human bodies. State-of-the-art data-driven approaches to model 3D garment deformations are trained using supervised strategies that require large datasets, usually obtained by expensive physics-based simulation methods or professional multi-camera capture setups. In contrast, we propose a new training scheme that removes the need for ground-truth samples, enabling self-supervised training of dynamic 3D garment deformations. Our key contribution is to realize that physics-based deformation models, traditionally solved in a frame-by-frame basis by implicit integrators, can be recasted as an optimization problem. We leverage such optimization-based scheme to formulate a set of physics-based loss terms that can be used to train neural networks without precomputing ground-truth data. This allows us to learn models for interactive garments, including dynamic deformations and fine wrinkles, with a two orders of magnitude speed up in training time compared to state-of-the-art supervised methods.

Recent advances in neural models have shown great results for virtual try-on (VTO) problems, where a 3D representation of a garment is deformed to fit a target body shape. However, current solutions are limited to a single garment layer, and cannot address the combinatorial complexity of mixing different garments. Motivated by this limitation, we investigate the use of neural fields for mix-and-match VTO, and identify and solve a fundamental challenge that existing neural-field methods cannot address: the interaction between layered neural fields. To this end, we propose a neural model that untangles layered neural fields to represent collision-free garment surfaces. The key ingredient is a neural untangling projection operator that works directly on the layered neural fields, not on explicit surface representations. Algorithms to resolve object-object interaction are inherently limited by the use of explicit geometric representations, and we show how methods that work directly on neural implicit representations could bring a change of paradigm and open the door to radically different approaches.

• Efficient acquisition of spectral BRDF materials from UAVs. • Ray-casting acceleration built around a feature-based clustering approach. • Path finding method that resembles the human’s brain navigation in known and unknown environments.

Abstract The simulation of complex deformation problems often requires enrichment techniques that introduce local high‐resolution detail on a generally coarse discretization. The use cases include spatial or temporal refinement of the discretization, the simulation of composite materials with phenomena occurring at different scales, or even codimensional simulation. We present an efficient simulation enrichment method for both local refinement of the discretization and codimensional effects. We dub our method Voronoi filters, as it combines two key computational elements. One is the use of kinematic filters to constrain coarse and fine deformations, and thus provide enrichment functions that are complementary to the coarse deformation. The other one is the use of a centroidal Voronoi discretization for the design of the enrichment functions, which adds high‐resolution detail in a compact manner while preserving the rigid modes of coarse deformation. We demonstrate our method on simulation examples of composite materials, hybrid triangle‐based and yarn‐level simulation of cloth, or enrichment of flesh simulation with high‐resolution detail.

Abstract We propose a novel formulation of elastic materials based on high‐order interpolants, which fits accurately complex elastic behaviors, but remains conservative. The proposed high‐order interpolants can be regarded as a high‐dimensional extension of radial basis functions, and they allow the interpolation of derivatives of elastic energy, in particular stress and stiffness. Given the proposed parameterization of elasticity models, we devise an algorithm to find optimal model parameters based on training data. We have tested our methodology for the homogenization of 2D microstructures, and we show that it succeeds to match complex behaviors with high accuracy.

Abstract Numerical coarsening methods offer an attractive methodology for fast simulation of objects with high‐resolution heterogeneity. However, they rely heavily on preprocessing, and are not suitable when objects undergo dynamic material or topology updates. We present methods that largely accelerate the two main processes of numerical coarsening, namely training data generation and the optimization of coarsening shape functions, and as a result we manage to leverage runtime numerical coarsening under local material updates. To accelerate the generation of training data, we propose a domain‐decomposition solver based on substructuring that leverages local factorizations. To accelerate the computation of coarsening shape functions, we propose a decoupled optimization of smoothness and data fitting. We evaluate quantitatively the accuracy and performance of our proposed methods, and we show that they achieve accuracy comparable to the baseline, albeit with speed‐ups of orders of magnitude. We also demonstrate our methods on example simulations with local material and topology updates.

We propose a novel method to machine-learn highly detailed, nonlinear contact deformations for real-time dynamic simulation. We depart from previous deformation-learning strategies, and model contact deformations in a contact-centric manner. This strategy shows excellent generalization with respect to the object's configuration space, and it allows for simple and accurate learning. We complement the contact-centric learning strategy with two additional key ingredients: learning a continuous vector field of contact deformations, instead of a discrete approximation; and sparsifying the mapping between the contact configuration and contact deformations. These two ingredients further contribute to the accuracy, efficiency, and generalization of the method. We integrate our learning-based contact deformation model with subspace dynamics, showing real-time dynamic simulations with fine contact deformation detail.

This paper introduces a methodology for inverse-modeling of yarn-level mechanics of cloth, based on the mechanical response of fabrics in the real world. We compiled a database from physical tests of several different knitted fabrics used in the textile industry. These data span different types of complex knit patterns, yarn compositions, and fabric finishes, and the results demonstrate diverse physical properties like stiffness, nonlinearity, and anisotropy. We then develop a system for approximating these mechanical responses with yarn-level cloth simulation. To do so, we introduce an efficient pipeline for converting between fabric-level data and yarn-level simulation, including a novel swatch-level approximation for speeding up computation, and some small-but-necessary extensions to yarn-level models used in computer graphics. The dataset used for this paper can be found at http://mslab.es/projects/YarnLevelFabrics.

We propose a new generative model for 3D garment deformations that enables us to learn, for the first time, a data-driven method for virtual try-on that effectively addresses garment-body collisions. In contrast to existing methods that require an undesirable postprocessing step to fix garment-body interpenetrations at test time, our approach directly outputs 3D garment configurations that do not collide with the underlying body. Key to our success is a new canonical space for garments that removes pose-and-shape deformations already captured by a new diffused human body model, which extrapolates body surface properties such as skinning weights and blendshapes to any 3D point. We leverage this representation to train a generative model with a novel self-supervised collision term that learns to reliably solve garment-body interpenetrations. We extensively evaluate and compare our results with recently proposed data-driven methods, and show that our method is the first to successfully address garment-body contact in unseen body shapes and motions, without compromising realism and detail.

Natural interaction with virtual objects relies on two major technology components: hand tracking and hand-object physics simulation. There are functional solutions for these two components, but their hand representations may differ in size and skeletal morphology, hence making the connection non-trivial. In this paper, we introduce a pose retargeting strategy to connect the tracked and simulated hand representations, and we have formulated and solved this hand retargeting as an optimization problem. We have also carried out a user study that demonstrates the effectiveness of our approach to enable fine manipulations that are slow and awkward with naïve approaches.

Abstract 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.>

GPUs have enabled a drastic change to computing environments, making massively parallel computing possible. Molecular dynamics is a perfect candidate problem for massively parallel computing, but to date it has not taken full advantage of multi-GPU environments due to the difficulty of partitioning molecular dynamics problems and exchanging problem data among compute nodes. These difficulties restrict the use of GPUs to only some of the computations in a full molecular dynamics problem, and hence prevent scalability beyond just a few GPUs. This work presents a scalable parallelization solution for the bonded and short-range forces present in a molecular dynamics problem. Together with existing solutions for long-range forces, it enables highly scalable, parallel molecular dynamics on multi-GPU computing environments. Specifically, the proposed solution divides the molecular volume into independent parts assigned to different GPUs, but it maintains a global bond structure that is efficiently exchanged when atoms move across GPUs. We demonstrate close-to-linear speedup of the proposed solution, simulating the dynamics of gigamolecules with 1 billion atoms on a computing environment with 96 GPUs, and obtaining superior performance to the well known molecular dynamics simulator NAMD.

This paper introduces a novel subspace method for the simulation of dynamic deformations. The method augments existing linear handle-based subspace formulations with nonlinear learning-based corrections parameterized by the same subspace. Together, they produce a compact nonlinear model that combines the fast dynamics and overall contact-based interaction of subspace methods, with the highly detailed deformations of learning-based methods. We propose a formulation of the model with nonlinear corrections applied on the local undeformed setting, and decoupling internal and external contact-driven corrections. We define a simple mapping of these corrections to the global setting, an efficient implementation for dynamic simulation, and a training pipeline to generate examples that efficiently cover the interaction space. Altogether, the method achieves unprecedented combination of speed and contact-driven deformation detail.

Abstract 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.

Abstract 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.

Abstract To deploy yarn‐level cloth simulations in production environments, it is paramount to design very efficient implementations, which mitigate the cost of the extremely high resolution. To this end, nodal discretizations aligned with the regularity of the fabric structure provide an optimal setting for efficient GPU implementations. However, nodal discretizations complicate the design of robust and controllable bending. In this paper, we address this challenge, and propose a model of bending that is both robust and controllable, and employs only nodal degrees of freedom. We extract information of yarn and fabric orientation implicitly from the nodal degrees of freedom, with no need to augment the model explicitly. But most importantly, and unlike previous formulations that use implicit orientations, the computation of bending forces bears no overhead with respect to other nodal forces such as stretch. This is possible by tracking optimal orientations efficiently. We demonstrate the impact of our bending model in examples with controllable anisotropy, as well as ironing, wrinkling, and plasticity.

Abstract 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.

Abstract 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.

This paper introduces a method to simulate complex rod assemblies and stacked layers with implicit contact handling, through Eulerian-on-Lagrangian (EoL) discretizations. Previous EoL methods fail to handle such complex situations, due to ubiquitous and intrinsic degeneracies in the contact geometry, which prevent the use of remeshing and make simulations unstable. We propose a novel mixed Eulerian-Lagrangian discretization that supports accurate and efficient contact as in EoL methods, but is transparent to internal rod forces, and hence insensitive to degeneracies. By combining the standard and novel EoL discretizations as appropriate, we derive mixed statics-dynamics equations of motion that can be solved in a unified manner with standard solvers. Our solution is simple and elegant in practice, and produces robust simulations on large-scale scenarios with complex rod arrangements and pervasive degeneracies. We demonstrate our method on multi-layer yarn-level cloth simulations, with implicit handling of both intra-and inter-layer contacts.

We present a method to render virtual touch, such that the stimulus produced by a tactile device on a user's skin matches the stimulus computed in a virtual environment simulation. To achieve this, we solve the inverse mapping from skin stimulus to device configuration thanks to a novel optimization algorithm. Within this algorithm, we use a device-skin simulation model to estimate rendered stimuli, we account for trajectory-dependent effects efficiently by decoupling the computation of the friction state from the optimization of device configuration, and we accelerate computations using a neural-network approximation of the device-skin model. Altogether, we enable real-time tactile rendering of rich interactions including smooth rolling, but also contact with edges, or frictional stick-slip motion. We validate our algorithm both qualitatively through user experiments, and quantitatively on a BioTac biomimetic finger sensor.

Tracking and reconstructing the 3D pose and geometry of two hands in interaction is a challenging problem that has a high relevance for several human-computer interaction applications, including AR/VR, robotics, or sign language recognition. Existing works are either limited to simpler tracking settings ( e.g. , considering only a single hand or two spatially separated hands), or rely on less ubiquitous sensors, such as depth cameras. In contrast, in this work we present the first real-time method for motion capture of skeletal pose and 3D surface geometry of hands from a single RGB camera that explicitly considers close interactions. In order to address the inherent depth ambiguities in RGB data, we propose a novel multi-task CNN that regresses multiple complementary pieces of information, including segmentation, dense matchings to a 3D hand model, and 2D keypoint positions, together with newly proposed intra-hand relative depth and inter-hand distance maps. These predictions are subsequently used in a generative model fitting framework in order to estimate pose and shape parameters of a 3D hand model for both hands. We experimentally verify the individual components of our RGB two-hand tracking and 3D reconstruction pipeline through an extensive ablation study. Moreover, we demonstrate that our approach offers previously unseen two-hand tracking performance from RGB, and quantitatively and qualitatively outperforms existing RGB-based methods that were not explicitly designed for two-hand interactions. Moreover, our method even performs on-par with depth-based real-time methods.

Abstract This paper presents a learning‐based clothing animation method for highly efficient virtual try‐on simulation. Given a garment, we preprocess a rich database of physically‐based dressed character simulations, for multiple body shapes and animations. Then, using this database, we train a learning‐based model of cloth drape and wrinkles, as a function of body shape and dynamics. We propose a model that separates global garment fit, due to body shape, from local garment wrinkles, due to both pose dynamics and body shape. We use a recurrent neural network to regress garment wrinkles, and we achieve highly plausible nonlinear effects, in contrast to the blending artifacts suffered by previous methods. At runtime, dynamic virtual try‐on animations are produced in just a few milliseconds for garments with thousands of triangles. We show qualitative and quantitative analysis of results.

We present a novel method for real-time pose and shape reconstruction of two strongly interacting hands. Our approach is the first two-hand tracking solution that combines an extensive list of favorable properties, namely it is marker-less, uses a single consumer-level depth camera, runs in real time, handles inter- and intra-hand collisions, and automatically adjusts to the user's hand shape. In order to achieve this, we embed a recent parametric hand pose and shape model and a dense correspondence predictor based on a deep neural network into a suitable energy minimization framework. For training the correspondence prediction network, we synthesize a two-hand dataset based on physical simulations that includes both hand pose and shape annotations while at the same time avoiding inter-hand penetrations. To achieve real-time rates, we phrase the model fitting in terms of a nonlinear least-squares problem so that the energy can be optimized based on a highly efficient GPU-based Gauss-Newton optimizer. We show state-of-the-art results in scenes that exceed the complexity level demonstrated by previous work, including tight two-hand grasps, significant inter-hand occlusions, and gesture interaction. 1

Natural hand-based interaction should feature hand motion that adapts smoothly to the tracked user's motion, reacts robustly to contact with objects in a virtual environment, and enables dexterous manipulation of these objects. In our work, we enable all these properties thanks to an efficient soft hand simulation model. This model integrates an articulated skeleton, nonlinear soft tissue and frictional contact, to provide the realism necessary for natural interaction. Robust and smooth interaction is made possible by simulating in a single energy minimization framework all the mechanical energy exchanges among elements of the hand: coupling between the hand's skeleton and the user's motion, constraints at skeletal joints, nonlinear soft skin deformation, coupling between the hand's skeleton and the soft skin, frictional contact between the skin and virtual objects, and coupling between a grasped object and other virtual objects. We have put our effort on describing all elements of the hand that provide for realism and natural interaction, while ensuring minimal and bounded computational cost, which is key for smooth and robust interaction. As a result, we accomplish hand simulation as an asset that can be connected to diverse input tracking devices, and seamlessly integrated in game engines for fast deployment in VR applications.

Abstract Damping determines how the energy in dynamic deformations is dissipated. The design of damping requires models where the behavior along deformation modes is easily controlled, while other motions are left unaffected. In this paper, we propose a framework for the design of damping using dissipation potentials formulated as functions of strain rate. We study simple parameterizations of the models, the application to continuum and discrete deformation models, and practical implications for implementation. We also study previous simple damping models, in particular we demonstrate limitations of Rayleigh damping. We analyze in detail the application of strain rate dissipation potentials to two highly different deformation models, StVK hyperlasticity and yarn‐level cloth with sliding persistent contacts. These deformation models are representative of the range of applicability of the damping model.

Abstract Many impressive fluid simulation methods have been presented in research papers before. These papers typically focus on demonstrating particular innovative features, but they do not meet in a comprehensive manner the production demands of actual VFX pipelines. VFX artists seek methods that are flexible, efficient, robust and scalable, and these goals often conflict with each other. In this paper, we present a multi‐phase particle‐based fluid simulation framework, based on the well‐known Position‐Based Fluids (PBF) method, designed to address VFX production demands. Our simulation framework handles multi‐phase interactions robustly thanks to a modified constraint formulation for density contrast PBF. And, it also supports the interaction of fluids sampled at different resolutions. We put special care on data structure design and implementation details. Our framework highlights cache‐efficient GPU‐friendly data structures, an improved spatial voxelization technique based on Z‐index sorting, tuned‐up simulation algorithms and two‐way‐coupled collision handling based on VDB fields. Altogether, our fluid simulation framework empowers artists with the efficiency, scalability and versatility needed for simulating very diverse scenes and effects.

Abstract Accurately modeling how light interacts with cloth is challenging, due to the volumetric nature of cloth appearance and its multiscale structure, where microstructures play a major role in the overall appearance at higher scales. Recently, significant effort has been put on developing better microscopic models for cloth structure, which have allowed rendering fabrics with unprecedented fidelity. However, these highly‐detailed representations still make severe simplifications on the scattering by individual fibers forming the cloth, ignoring the impact of fibers' shape, and avoiding to establish connections between the fibers' appearance and their optical and fabrication parameters. In this work we put our focus in the scattering of individual cloth fibers; we introduce a physically‐based scattering model for fibers based on their low‐level optical and geometric properties, relying on the extensive textile literature for accurate data. We demonstrate that scattering from cloth fibers exhibits much more complexity than current fiber models, showing important differences between cloth type, even in averaged conditions due to longer views. Our model can be plugged in any framework for cloth rendering, matches scattering measurements from real yarns, and is based on actual parameters used in the textile industry, allowing predictive bottom‐up definition of cloth appearance.

Abstract Most popular methods in cloth rendering rely on volumetric data in order to model complex optical phenomena such as sub‐surface scattering. These approaches are able to produce very realistic illumination results, but their volumetric representations are costly to compute and render, forfeiting any interactive feedback. In this paper, we introduce a method based on the Graphics Processing Unit (GPU) for voxelization and visualization, suitable for both interactive and offline rendering. Recent features in the OpenGL model, like the ability to dynamically address arbitrary buffers and allocate bindless textures, are combined into our pipeline to interactively voxelize millions of polygons into a set of large three‐dimensional (3D) textures (>109 elements), generating a volume with sub‐voxel accuracy, which is suitable even for high‐density woven cloth such as linen.

The simulation of high viscoelasticity poses important computational challenges. One is the difficulty to robustly measure strain and its derivatives in a medium without permanent structure. Another is the high stiffness of the governing differential equations. Solutions that tackle these challenges exist, but they are computationally slow. We propose a constraint-based model of viscoelasticity that enables efficient simulation of highly viscous and viscoelastic phenomena. Our model reformulates, in a constraint-based fashion, a constitutive model of viscoelasticity for polymeric fluids, which defines simple governing equations for a conformation tensor. The model can represent a diverse palette of materials, spanning elastoplastic, highly viscous, and inviscid liquid behaviors. In addition, we have designed a constrained dynamics solver that extends the position-based dynamics method to handle efficiently both position-based and velocity-based constraints. We show results that range from interactive simulation of viscoelastic effects to large-scale simulation of high viscosity with competitive performance.

We propose a computational tool for designing Kirchhoff-Plateau Surfaces---planar rod networks embedded in pre-stretched fabric that deploy into complex, three-dimensional shapes. While Kirchhoff-Plateau Surfaces offer an intriguing and expressive design space, navigating this space is made difficult by the highly nonlinear nature of the underlying mechanical problem. In order to tackle this challenge, we propose a user-guided but computer-assisted approach that combines an efficient forward simulation model with a dedicated optimization algorithm in order to implement a powerful set of design tools. We demonstrate our method by designing a diverse set of complex-shaped Kirchhoff-Plateau Surfaces, each validated through physically-fabricated prototypes.

Cloth is made of yarns that are stitched together forming semi-regular patterns. Due to the complexity of stitches and patterns, the macroscopic behavior of cloth is dictated by the contact interactions between yarns, not by the mechanical properties of yarns alone. The computation of cloth mechanics at the yarn level appears as a computationally complex and costly process at first sight, due to the need to resolve many fine-scale contact interactions. We propose instead an efficient representation of cloth at the yarn level that treats yarn-yarn contacts as persistent, but with the possibility to slide, thereby avoiding expensive contact handling altogether. We introduce a compact representation of yarn geometry and kinematics, capturing the essential deformation modes of yarn crossings, loops, stitches, and stacks, with a minimum cost. Based on this representation, we design force models that reproduce the characteristic macroscopic behavior of yarn-based fabrics. Our approach is suited for both woven and knitted fabrics. We demonstrate the efficiency of our method on simulations with millions of degrees of freedom (hundreds of thousands of yarn loops), almost one order of magnitude faster than previous techniques. We also compare the different macroscopic behavior under woven and knitted patterns with the same yarn density.

The papers in this special section were presented at the 15th Annual ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA 2016), which was held in Zurich, Switzerland on July 11-13, 2016.

Abstract In this paper, we present a method to model hyperelasticity that is well suited for representing the nonlinearity of real‐world objects, as well as for estimating it from deformation examples. Previous approaches suffer several limitations, such as lack of integrability of elastic forces, failure to enforce energy convexity, lack of robustness of parameter estimation, or difficulty to model cross‐modal effects. Our method avoids these problems by relying on a general energy‐based definition of elastic properties. The accuracy of the resulting elastic model is maximized by defining an additive model of separable energy terms, which allow progressive parameter estimation. In addition, our method supports efficient modeling of extreme nonlinearities thanks to energy‐limiting constraints. We combine our energy‐based model with an optimization method to estimate model parameters from force‐deformation examples, and we show successful modeling of diverse deformable objects, including cloth, human finger skin, and internal human anatomy in a medical imaging application.

We propose a method to simulate the rich, scale-dependent dynamics of water waves. Our method preserves the dispersion properties of real waves, yet it supports interactions with obstacles and is computationally efficient. Fundamentally, it computes wave accelerations by way of applying a dispersion kernel as a spatially variant filter, which we are able to compute efficiently using two core technical contributions. First, we design novel, accurate, and compact pyramid kernels which compensate for low-frequency truncation errors. Second, we design a shadowed convolution operation that efficiently accounts for obstacle interactions by modulating the application of the dispersion kernel. We demonstrate a wide range of behaviors, which include capillary waves, gravity waves, and interactions with static and dynamic obstacles, all from within a single simulation.

Crumpling a thin sheet produces a characteristic sound, comprised of distinct clicking sounds corresponding to buckling events. We propose a physically based algorithm that automatically synthesizes crumpling sounds for a given thin shell animation. The resulting sound is a superposition of individually synthesized clicking sounds corresponding to visually significant and insignificant buckling events. We identify visually significant buckling events on the dynamically evolving thin surface mesh, and instantiate visually insignificant buckling events via a stochastic model that seeks to mimic the power-law distribution of buckling energies observed in many materials. In either case, the synthesis of a buckling sound employs linear modal analysis of the deformed thin shell. Because different buckling events in general occur at different deformed configurations, the question arises whether the calculation of linear modes can be reused. We amortize the cost of the linear modal analysis by dynamically partitioning the mesh into nearly rigid pieces: the modal analysis of a rigidly moving piece is retained over time, and the modal analysis of the assembly is obtained via Component Mode Synthesis (CMS). We illustrate our approach through a series of examples and a perceptual user study, demonstrating the utility of the sound synthesis method in producing realistic sounds at practical computation times.

This paper presents a numerical coarsening method for corotational elasticity, which enables interactive large deformation of high-resolution heterogeneous objects. Our method derives a coarse elastic model from a high-resolution discretization of corotational elasticity with high-resolution boundary conditions. This is in contrast to previous coarsening methods, which derive a coarse elastic model from an unconstrained high-resolution discretization of regular linear elasticity, and then apply corotational computations directly on the coarse setting. We show that previous approaches fail to handle high-resolution boundary conditions correctly, suffering accuracy and robustness problems. Our method, on the other hand, supports efficiently accurate high-resolution boundary conditions, which are fundamental for rich interaction with high-resolution heterogeneous models. We demonstrate the potential of our method for interactive deformation of complex medical imaging data sets.

The computation of skin forces and deformations for tactile rendering requires an accurate model of the extremely nonlinear behavior of the skin. In this work, we investigate the characterization of finger mechanics with the goal of designing accurate nonlinear models for tactile rendering. First, we describe a measurement setup that enables the acquisition of contact force and contact area in the context of controlled finger indentation experiments. Second, we describe an optimization procedure that estimates the parameters of strain-limiting deformation models that match best the acquired data. We show that the acquisition setup allows the measurement of force and area information with high repeatability, and the estimation method reaches nonlinear models that match the measured data with high accuracy.

This paper introduces a tactile rendering algorithm for wearable cutaneous devices that stimulate the skin through local contact surface modulation. The first step in the algorithm simulates contact between a skin model and virtual objects, and computes the contact surface to be rendered. The accuracy of this surface is maximized by simulating soft skin with its characteristic nonlinear behavior. The second step takes the desired contact surface as input, and computes the device configuration by solving an optimization problem, i.e., minimizing the deviation between the contact surface in the virtual environment and the contact surface rendered by the device. The method is implemented on a thimble-like wearable device.

We present a computational tool for fabrication-oriented design of flexible rod meshes. Given a deformable surface and a set of deformed poses as input, our method automatically computes a printable rod mesh that, once manufactured, closely matches the input poses under the same boundary conditions. The core of our method is formed by an optimization scheme that adjusts the cross-sectional profiles of the rods and their rest centerline in order to best approximate the target deformations. This approach allows us to locally control the bending and stretching resistance of the surface with a single material, yielding high design flexibility and low fabrication cost.

The papers in this special section were presented at the ACM Symposium on Interactive 3D Graphics and Games (I3D) in 2014 that was held from March 14-16 in San Francisco, California. I3D focuses on real-time rendering, animation, and interaction techniques. Games are not the only application for these kinds of interactive methods, but are certainly a significant user of them. Consequently, the quality, time, space, stability, and predictability constraints faced by game development are an important factor in much of the work presented at I3D.

Interactive dexterous manipulation of virtual objects remains a complex challenge that requires both appropriate hand models and accurate physically-based simulation of interactions. In this paper, we propose an approach based on novel aggregate constraints for simulating dexterous grasping using soft fingers. Our approach aims at improving the computation of contact mechanics when many contact points are involved, by aggregating the multiple contact constraints into a minimal set of constraints. We also introduce a method for non-uniform pressure distribution over the contact surface, to adapt the response when touching sharp edges. We use the Coulomb-Contensou friction model to efficiently simulate tangential and torsional friction. We show through different use cases that our aggregate constraint formulation is well-suited for simulating interactively dexterous manipulation of virtual objects through soft fingers, and efficiently reduces the computation time of constraint solving.

We propose a novel algorithm to simulate brittle fracture. It augments previous methods based on Voronoi diagrams, improving their versatility and their ability to adapt fracture patterns automatically to diverse collision scenarios and object properties. We cast brittle fracture as the computation of a high-dimensional Centroidal Voronoi Diagram (CVD), where the distribution of fracture fragments is guided by the deformation field of the fractured object. By formulating the problem in high dimensions, we support robustly object and crack concavities, as well as intuitive artist control. We further accelerate the fracture animation process with example-based learning of the fracture degree, and a highly parallel tessellation algorithm. As a result, we obtain fast animations of detailed and rich fractures, with fracture patterns that adapt to each particular collision scenario.

Abstract The dynamic simulation of mechanical effects has a long history in computer graphics. The classical methods in this field discretize Newton's second law in a variety of Lagrangian or Eulerian ways, and formulate forces appropriate for each mechanical effect: joints for rigid bodies; stretching, shearing or bending for deformable bodies and pressure, or viscosity for fluids, to mention just a few. In the last years, the class of position‐based methods has become popular in the graphics community. These kinds of methods are fast, stable and controllable which make them well‐suited for use in interactive environments. Position‐based methods are not as accurate as force‐based methods in general but they provide visual plausibility. Therefore, the main application areas of these approaches are virtual reality, computer games and special effects in movies. This state‐of‐the‐art report covers the large variety of position‐based methods that were developed in the field of physically based simulation. We will introduce the concept of position‐based dynamics, present dynamic simulation based on shape matching and discuss data‐driven upsampling approaches. Furthermore, we will present several applications for these methods.

The large-scale mechanical behavior of woven cloth is determined by the mechanical properties of the yarns, the weave pattern, and frictional contact between yarns. Using standard simulation methods for elastic rod models and yarn-yarn contact handling, the simulation of woven garments at realistic yarn densities is deemed intractable. This paper introduces an efficient solution for simulating woven cloth at the yarn level. Central to our solution is a novel discretization of interlaced yarns based on yarn crossings and yarn sliding, which allows modeling yarn-yarn contact implicitly, avoiding contact handling at yarn crossings altogether. Combined with models for internal yarn forces and inter-yarn frictional contact, as well as a massively parallel solver, we are able to simulate garments with hundreds of thousands of yarn crossings at practical frame-rates on a desktop machine, showing combinations of large-scale and fine-scale effects induced by yarn-level mechanics.

We present an efficient new subspace method for simulating the self-contact of articulated deformable bodies, such as characters. Self-contact is highly structured in this setting, as the limited space of possible articulations produces a predictable set of coherent collisions. Subspace methods can leverage this coherence, and have been used in the past to accelerate the collision detection stage of contact simulation. We show that these methods can be used to accelerate the entire contact computation, and allow self-contact to be resolved without looking at all of the contact points . Our analysis of the problem yields a broader insight into the types of non-linearities that subspace methods can efficiently approximate, and leads us to design a pose-space cubature scheme. Our algorithm accelerates self-contact by up to an order of magnitude over other subspace simulations, and accelerates the overall simulation by two orders of magnitude over full-rank simulations. We demonstrate the simulation of high resolution (100K -- 400K elements) meshes in self-contact at interactive rates (5.8 -- 50 FPS).

In complex scenes with many objects, collision detection plays a key role in the simulation performance. This is particularly true in fracture simulation for two main reasons. One is that fracture fragments tend to exhibit very intensive contact, and the other is that collision detection data structures for new fragments need to be computed on the fly. In this paper, we present novel collision detection algorithms and data structures for real-time simulation of fracturing rigid bodies. We build on a combination of well-known efficient data structures, namely, distance fields and sphere trees, making our algorithm easy to integrate on existing simulation engines. We propose novel methods to construct these data structures, such that they can be efficiently updated upon fracture events and integrated in a simple yet effective self-adapting contact selection algorithm. Altogether, we drastically reduce the cost of both collision detection and collision response. We have evaluated our global solution for collision detection on challenging scenarios, achieving high frame rates suited for hard real-time applications such as video games or haptics. Our solution opens promising perspectives for complex fracture simulations involving many dynamically created rigid objects.

Haptic interaction with different types of materials in the same scene is a challenging task, mainly due to the specific coupling mechanisms that are usually required for either fluid, deformable or rigid media. Dynamically-changing materials, such as melting or freezing objects, present additional challenges by adding another layer of complexity in the interaction between the scene and the haptic proxy. In this paper, we address these issues through a common simulation framework, based on Smoothed-Particle Hydrodynamics, and enable haptic interaction simultaneously with fluid, elastic and rigid bodies, as well as their melting or freezing. We introduce a mechanism to deal with state changes, allowing the perception of haptic feedback during the process, and a set of dynamic mechanisms to enrich the interaction through the proxy. We decouple the haptic and visual loops through a dual GPU implementation. An initial evaluation of the approach is performed through performance and feedback measurements, as well as a small user study assessing the capability of users to recognize the different states of matter they interact with.

The command of haptic devices for rendering direct interaction with the hand requires thorough knowledge of the forces and deformations caused by contact interactions on the fingers. In this paper, we propose an algorithm to simulate nonlinear elasticity under frictional contact, with the goal of establishing a model-based strategy to command haptic devices and to render direct hand interaction. The key novelty in our algorithm is an approach to model the extremely nonlinear elasticity of finger skin and flesh using strain-limiting constraints, which are seamlessly combined with frictional contact constraints in a standard constrained dynamics solver. We show that our approach enables haptic rendering of rich and compelling deformations of the fingertip.

“Haptic rendering” or “haptic display” can be broadly defined as conveying information about virtual objects or data to a user through the sense of touch. Among all applications of haptic rendering, force-feedback display of contact interactions with rigid and deformable virtual models through the sense of touch has matured considerably over the last decade. In this paper, we present a general framework for force-feedback display of rigid and virtual environments, and we outline its major building blocks. We focus on computational aspects, and we classify algorithms and representations successfully used in the three major subproblems of force-feedback display: collision detection, dynamics simulation, and constrained optimization. In addition, force-feedback display is an integral part of a multimodal experience, often involving both visual and auditory display; therefore, we also discuss the choice of algorithms and representations for force feedback as a part of multimodal display.

Force-deformation measurements of cloth exhibit significant hysteresis, and many researchers have identified internal friction as the source of this effect. However, it has not been incorporated into computer animation models of cloth. In this paper, we propose a model of internal friction based on an augmented reparameterization of Dahl's model, and we show that this model provides a good match to several important features of cloth hysteresis even with a minimal set of parameters. We also propose novel parameter estimation procedures that are based on simple and inexpensive setups and need only sparse data, as opposed to the complex hardware and dense data acquisition of previous methods. Finally, we provide an algorithm for the efficient simulation of internal friction, and we demonstrate it on simulation examples that show disparate behavior with and without internal friction.

Visuo-haptic mixed reality consists of adding to a real scene the ability to see and touch virtual objects. It requires the use of see-through display technology for visually mixing real and virtual objects, and haptic devices for adding haptic interaction with the virtual objects. Unfortunately, the use of commodity haptic devices poses obstruction and misalignment issues that complicate the correct integration of a virtual tool and the user's real hand in the mixed reality scene. In this work, we propose a novel mixed reality paradigm where it is possible to touch and see virtual objects in combination with a real scene, using commodity haptic devices, and with a visually consistent integration of the user's hand and the virtual tool. We discuss the visual obstruction and misalignment issues introduced by commodity haptic devices, and then propose a solution that relies on four simple technical steps: color-based segmentation of the hand, tracking-based segmentation of the haptic device, background repainting using image-based models, and misalignment-free compositing of the user's hand. We have developed a successful proof-of-concept implementation, where a user can touch virtual objects and interact with them in the context of a real scene, and we have evaluated the impact on user performance of obstruction and misalignment correction.

The simulation of cloth with rich folds and wrinkles is a computationally expensive process. In this paper, we introduce an example-based algorithm for fast animation of plausible cloth wrinkles. Our algorithm does not depend on a character's pose, therefore it is valid for loose dresses, curtains, etc., not just cloth defined by skinning techniques. Central to our approach is a correspondence between low and high-resolution cloth deformations, both at the training and synthesis stages. Based on this correspondence, we define an algorithm for synthesizing cloth wrinkles as a function of the deformation of a low-resolution cloth and a set of example poses. We demonstrate the animation of plausible high-resolution wrinkles at high frame rates, suitable for interactive applications such as video games.

Abstract Progress in cloth simulation for computer animation and apparel design has led to a multitude of deformation models, each with its own way of relating geometry, deformation, and forces. As simulators improve, differences between these models become more important, but it is difficult to choose a model and a set of parameters to match a given real material simply by looking at simulation results. This paper provides measurement and fitting methods that allow nonlinear models to be fit to the observed deformation of a particular cloth sample. Unlike standard textile testing, our system measures complex 3D deformations of a sheet of cloth, not just one‐dimensional force‐displacement curves, so it works under a wider range of deformation conditions. The fitted models are then evaluated by comparison to measured deformations with motions very different from those used for fitting.

We present a simple algorithm to compute continuous penalty forces to determine collision response between rigid and deformable models bounded by triangle meshes. Our algorithm computes a well-behaved solution in contrast to the traditional stability and robustness problems of penalty methods, induced by force discontinuities. We trace contact features along their deforming trajectories and accumulate penalty forces along the penetration time intervals between the overlapping feature pairs. Moreover, we present a closed-form expression to compute the continuous and smooth collision response. Our method has very small additional overhead compared to previous penalty methods, and can significantly improve the stability and robustness. We highlight its benefits on several benchmarks.

Operations such as object manipulation and palpation rely on the fine perception of contact forces, both in time and space. Haptic simulation of grasping, with the rendering of contact forces resulting from the manipulation of virtual objects, requires realistic yet interactive models of hand mechanics. This paper presents a model for interactive simulation of the skeletal and elastic properties of a human hand, allowing haptic grasping of virtual objects with soft finger contact. The novel aspects of the model consist of a simple technique to couple skeletal and elastic elements, an efficient dynamics solver in the presence of joints and contact constraints, and an algorithm that connects the simulation to a haptic device.

Neural connections in the brain are arranged in dense, complex networks of filiform (i.e. thread-like) structures. Navigation along complex filiform networks entails problems including path following, path selection at complex branching nodes, and the combination of large-scale with fine-scale exploration. This work proposes haptic interaction techniques to aid in the navigation along such complex filiform structures. It introduces a constraint-based path following approach with smooth node transition and intuitive branch selection during fine-scale exploration, as well as user-friendly camera control and visual aids for large-scale exploration. We demonstrate the successful application of these techniques to the analysis and understanding of dense neural networks.

Abstract Modal analysis techniques are often used to animate deformable objects in real time. In order to achieve performance improvements, the degrees of freedom of the problem may be reduced by using the main global deformations alone. However, this can lead to a reduction in quality and realism due to the lack of local behaviors. To solve this problem, we present a new method to add local deformations to modal analysis simulations. We perceptually evaluate our method with a set of experiments, thereby deriving guidelines for when and how local deformations can best be used. Copyright © 2010 John Wiley & Sons, Ltd.

This paper introduces a data-driven process for designing and fabricating materials with desired deformation behavior. Our process starts with measuring deformation properties of base materials. For each base material we acquire a set of example deformations, and we represent the material as a non-linear stress-strain relationship in a finite-element model. We have validated our material measurement process by comparing simulations of arbitrary stacks of base materials with measured deformations of fabricated material stacks. After material measurement, our process continues with designing stacked layers of base materials. We introduce an optimization process that finds the best combination of stacked layers that meets a user's criteria specified by example deformations. Our algorithm employs a number of strategies to prune poor solutions from the combinatorial search space. We demonstrate the complete process by designing and fabricating objects with complex heterogeneous materials using modern multi-material 3D printers.

Collision detection is a problem that has often been addressed efficiently with the use of hierarchical culling data structures. In the subproblem of self-collision detection for triangle meshes, however, such hierarchical data structures lose much of their power, because triangles adjacent to each other cannot be distinguished from actually colliding ones unless individually tested. Shape regularity of surface patches, described in terms of orientation and contour conditions, was proposed long ago as a culling criterion for hierarchical self-collision detection. However, to date, algorithms based on shape regularity had to trade conservativeness for efficiency, because there was no known algorithm for efficiently performing 2D contour self-intersection tests. In this paper, we introduce a star-contour criterion that is amenable to hierarchical computations. Together with a thorough analysis of the tree traversal process in hierarchical self-collision detection, it has led us to novel hierarchical data structures and algorithms for efficient yet conservative self-collision detection. We demonstrate the application of our algorithm to several example animations, and we show that it consistently outperforms other approaches.

Visuo-haptic mixed reality consists of adding to a real scene the ability to see and touch virtual objects. It requires the use of see-through display technology for visually mixing real and virtual objects, and haptic devices for adding haptic interaction with the virtual objects. However, haptic devices tend to be bulky items that appear in the field of view of the user. In this work, we propose a novel mixed reality paradigm where it is possible to touch and see virtual objects in combination with a real scene, but without visual obtrusion produced by the haptic device. This mixed reality paradigm relies on the following three technical steps: tracking of the haptic device, visual deletion of the device from the real scene, and background completion using image-based models. We have developed a successful proof-of-concept implementation, where a user can touch virtual objects in the context of a real scene.

We present a method to control the behavior of elastic, deformable material in a dynamic simulation. We introduce dynamic morph targets, the equivalent in dynamic simulation to the geometric morph targets in (quasi-static) modeling. Dynamic morph targets define the pose-dependent physical state of soft objects, including surface deformation and elastic and inertial properties. Given these morph targets, our algorithm then derives a dynamic model that can be simulated in time-pose-space, interpolating the dynamic morph targets at the input poses. Our method easily integrates with current modeling and animation pipelines: at different poses, an artist simply provides a set of dynamic morph targets. Whether these input states are physically plausible is completely up to the artist. The resulting deformable models expose fully dynamic, pose-dependent behavior, driven by the artist-provided morph targets, complete with inertial effects.

Abstract We present an algorithm for robust and efficient contact handling of deformable objects. By being aware of the internal dynamics of the colliding objects, our algorithm provides smooth rolling and sliding, stable stacking, robust impact handling, and seamless coupling of heterogeneous objects, all in a unified manner. We achieve dynamicsawareness through a constrained dynamics formulation with implicit complementarity constraints, and we present two major contributions that enable an efficient solution of the constrained dynamics problem: a time stepping algorithm that robustly ensures non‐penetration and progressively refines the formulation of constrained dynamics, and a new solver for large mixed linear complementarity problems, based on iterative constraint anticipation. We show the application of our algorithm in challenging scenarios such as multi‐layered cloth moving at high velocities, or colliding deformable solids simulated with large time steps.

This paper introduces a data-driven representation and modeling technique for simulating non-linear heterogeneous soft tissue. It simplifies the construction of convincing deformable models by avoiding complex selection and tuning of physical material parameters, yet retaining the richness of non-linear heterogeneous behavior. We acquire a set of example deformations of a real object, and represent each of them as a spatially varying stress-strain relationship in a finite-element model. We then model the material by non-linear interpolation of these stress-strain relationships in strain-space. Our method relies on a simple-to-build capture system and an efficient run-time simulation algorithm based on incremental loading, making it suitable for interactive computer graphics applications. We present the results of our approach for several non-linear materials and biological soft tissue, with accurate agreement of our model to the measured data.

The simulation of fracture leads to collision-intensive situations that call for efficient collision detection algorithms and data structures. Bounding volume hierarchies (BVHs) are a popular approach for accelerating collision detection, but they rarely see application in fracture simulations, due to the dynamic creation and deletion of geometric primitives. We propose the use of balanced trees for storing BVHs, as well as novel algorithms for dynamically restructuring them in the presence of progressive or instantaneous fracture. By paying a small loss of fitting quality compared with complete reconstruction, we achieve more than one order of magnitude speedup in the update of BVHs

Abstract Fast contact handling of soft articulated characters is a computationally challenging problem, in part due to complex interplay between skeletal and surface deformation. We present a fast, novel algorithm based on a layered representation for articulated bodies that enables physically‐plausible simulation of animated characters with a high‐resolution deformable skin in real time. Our algorithm gracefully captures the dynamic skeleton‐skin interplay through a novel formulation of elastic deformation in the pose space of the skinned surface. The algorithm also overcomes the computational challenges by robustly decoupling skeleton and skin computations using careful approximations of Schur complements, and efficiently performing collision queries by exploiting the layered representation. With this approach, we can simultaneously handle large contact areas, produce rich surface deformations, and capture the collision response of a character/s skeleton.

Abstract We introduce a new appearance‐modeling paradigm for synthesizing the internal structure of a 3D model from photographs of a few cross‐sections of a real object. When the internal surfaces of the 3D model are revealed as it is cut, carved, or simply clipped, we synthesize their texture from the input photographs. Our texture synthesis algorithm is best classified as a morphing technique, which efficiently outputs the texture attributes of each surface point on demand. For determining source points and their weights in the morphing algorithm, we propose an interpolation domain based on BSP trees that naturally resembles planar splitting of real objects. In the context of the interpolation domain, we define efficient warping and morphing operations that allow for real‐time synthesis of textures. Overall, our modeling paradigm, together with its realization through our texture morphing algorithm, allow users to author 3D models that reveal highly realistic internal surfaces in a variety of artistic flavors.

We present a novel multi-scale representation and acquisition method for the animation of high-resolution facial geometry and wrinkles. We first acquire a static scan of the face including reflectance data at the highest possible quality. We then augment a traditional marker-based facial motion-capture system by two synchronized video cameras to track expression wrinkles. The resulting model consists of high-resolution geometry, motion-capture data, and expression wrinkles in 2D parametric form. This combination represents the facial shape and its salient features at multiple scales. During motion synthesis the motion-capture data deforms the high-resolution geometry using a linear shell-based mesh-deformation method. The wrinkle geometry is added to the facial base mesh using nonlinear energy optimization. We present the results of our approach for performance replay as well as for wrinkle editing.

This paper presents a modular algorithm for six-degree-of-freedom (6-DOF) haptic rendering. The algorithm is aimed to provide transparent manipulation of rigid models with a high polygon count. On the one hand, enabling a stable display is simplified by exploiting the concept of virtual coupling and employing passive implicit integration methods for the simulation of the virtual tool. On the other hand, transparency is enhanced by maximizing the update rate of the simulation of the virtual tool, and thereby the coupling impedance, and allowing for stable simulation with small mass values. The combination of a linearized contact model that frees the simulation from the computational bottleneck of collision detection, with penalty-based collision response well suited for fixed time-stepping, guarantees that the motion of the virtual tool is simulated at the same high rate as the synthesis of feedback force and torque. Moreover, sensation-preserving multiresolution collision detection ensures a fast update of the linearized contact model in complex contact scenarios, and a novel contact clustering technique alleviates possible instability problems induced by penalty-based collision response

Surface texture is among the most salient haptic characteristics of objects; it can induce vibratory contact forces that lead to perception of roughness. We present a new algorithm to display haptic texture information resulting from the interaction between two textured objects. We compute contact forces and torques using low-resolution geometric representations along with texture images that encode surface details. We also introduce a novel force model based on directional penetration depth and describe an efficient implementation on programmable graphics hardware that enables interactive haptic texture rendering of complex models. Our force model takes into account important factors identified by psychophysics studies and is able to haptically display interaction due to fine surface textures that previous algorithms do not capture.

Abstract We present an algorithm for fast computation of discretized 3D distance fields using graphics hardware. Given a set of primitives and a distance metric, our algorithm computes the distance field for each slice of a uniform spatial grid baly rasterizing the distance functions of the primitives. We compute bounds on the spatial extent of the Voronoi region of each primitive. These bounds are used to cull and clamp the distance functions rendered for each slice. Our algorithm is applicable to all geometric models and does not make any assumptions about connectivity or a manifold representation. We have used our algorithm to compute distance fields of large models composed of tens of thousands of primitives on high resolution grids. Moreover, we demonstrate its application to medial axis evaluation and proximity computations. As compared to earlier approaches, we are able to achieve an order of magnitude improvement in the running time. Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Distance fields, Voronoi regions, graphics hardware, proximity computations

No abstract available.

We introduce a novel "sensation preserving" simplification algorithm for faster collision queries between two polyhedral objects in haptic rendering. Given a polyhedral model, we construct a multiresolution hierarchy using " filtered edge collapse", subject to constraints imposed by collision detection. The resulting hierarchy is then used to compute fast contact response for haptic display. The computation model is inspired by human tactual perception of contact information. We have successfully applied and demonstrated the algorithm on a time-critical collision query framework for haptically displaying complex object-object interaction. Compared to existing exact contact query algorithms, we observe noticeable performance improvement in update rates with little degradation in the haptic perception of contacts.

We present a survey of our recent research on the development of haptic interfaces for simulating creative processes with digital media, including 3D multiresolution modeling and 2D and 3D painting. We discuss the design issues involved and lessons learned. Based on the preliminary user studies, we observe that haptic interfaces can improve the level of usability of digital design systems and assist in capturing the feel of creative processes.

We present a system, ArtNova, for 3D model design with a haptic interface. ArtNova offers the novel capability of interactively applying textures onto 3D surfaces directly by brush strokes, with the orientation of the texture determined the stroke. Building upon the framework of inTouch (Gregory et al., 2000), it further provides an intuitive physically-based force response when deforming a model. This system also uses a user-centric viewing technique that seamlessly integrates the haptic and visual presentation, by taking into account the user's haptic manipulation in dynamically determining the new viewpoint locations. Our algorithm permits automatic placement of the user viewpoint to navigate about the object. ArtNova has been tested by several users and they were able to start modeling and painting with just a few minutes of training. Preliminary user feedback indicates promising potential for 3D texture painting and modeling.

We present several techniques for user-centric viewing of the virtual objects or datasets under haptic exploration and manipulation. Depending on the type of tasks performed by the user, our algorithms compute automatic placement of the user viewpoint to navigate through the scene, to display the near-optimal views, and to reposition the viewpoint for haptic visualization. This is accomplished by conjecturing the user's intent based on the user's actions, the object geometry, and intra- and inter-object occlusion relationships. These algorithms have been implemented and interfaced with both a 3-DOF and a 6-DOF PHANToM arms. We demonstrate their application on haptic exploration and visualization of a complex structure, as well as multiresolution modeling and 3D painting with a haptic interface.