FLSH -- Friendly Library for the Simulation of Humans

Computer models of humans are ubiquitous throughout computer animation and computer vision. However, these models rarely represent the dynamics of human motion, as this requires adding a complex layer that solves body motion in response to external interactions and according to the laws of physics. FLSH is a library that facilitates this task for researchers and developers who are not interested in the nuisances of physics simulation, but want to easily integrate dynamic humans in their applications. FLSH provides easy access to three flavors of body physics, with different features and computational complexity: skeletal dynamics, full soft-tissue dynamics, and reduced-order modeling of soft-tissue dynamics. In all three cases, the simulation models are built on top of the pseudo-standard SMPL parametric body model.Comment: Project website: https://gitlab.com/PabloRamonPrieto/fls

Haptic rendering of complex deformations through handle-space force linearization

The force-update-rate requirements of transparent rendering of vir-tual environments are in conflict with the computational cost re-quired for computing complex interactions between deforming ob-jects. In this paper we introduce a novel method for satisfying high force update rates with deformable objects, yet retaining the visual quality of complex deformations and interactions. The objects that are haptically manipulated may have many de-grees of freedom, but haptic interaction is often implemented in practice through low-dimensional force-feedback devices. We ex-ploit the low-dimensional domain of the interaction for devising a novel linear approximation of interaction forces that can be ef-ficiently evaluated at force-update rates. Moreover, our linearized force model is time-implicit, which implies that it accounts for con-tact constraints and the internal dynamics of deforming objects. In this paper we show examples of haptic interaction in complex sit-uations such as large deformations, collision between deformable objects (with friction), or even self-collision

Dispersion kernels for water wave simulation

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.Funding Sources: European Research Council; Spanish Ministry of EconomyPeer Reviewe

Capture and Modeling of Non-Linear Heterogeneous Soft Tissue

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.Engineering and Applied Science

Conformation constraints for efficient viscoelastic fluid simulation

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

Perceived match between own and observed models' bodies: influence of face, viewpoints, and body size

People are generally unable to accurately determine their own body measurements and to translate this knowledge to identifying a model/avatar that best represents their own body. This inability has not only been related to health problems (e.g. anorexia nervosa), but has important practical implications as well (e.g. online retail). Here we aimed to investigate the influence of three basic visual features—face presence, amount of viewpoints, and observed model size—on the perceived match between own and observed models' bodies and on attitudes towards these models. Models were real-life models (Experiment 1) or avatar models based on participants' own bodies (Experiment 2). Results in both experiments showed a strong effect of model size, irrespective of participants' own body measurements. When models were randomly presented one by one, participants gave significantly higher ratings to smaller- compared to bigger-sized models. The reverse was true, however, when participants observed and compared models freely, suggesting that the mode of presentation affected participants' judgments. Limited evidence was found for an effect of facial presence or amount of viewpoints. These results add evidence to research on visual features affecting the ability to match observed bodies with own body image, which has biological, clinical, and practical implications.ATJ and LDC were supported by Ministerio de Economía, Industria y Competitividad of Spain Ramón y Cajal Grant RYC-2014-15421. LDC was also supported by Ministerio de Ciencia, Innovación y Universidades Juan de la Cierva-Incorporación Grant IJC2018-038347-I

Modeling and estimation of internal friction in cloth

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

Design and fabrication of materials with desired deformation behavior

Figure 1: Two examples of real and replicated objects. Thanks to our data-driven process, we are able to measure, simulate, and obtain material combinations of non-linear base materials that match a desired deformation behavior. We can then print those objects with multi-material 3D printers using two materials (blue and black) with varying internal microstructure. This paper introduces a data-driven process for designing and fab-ricating 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 measure-ment 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 pro-cess 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