Yunfei Bai, Wenhao Yu, and C. Karen Liu
This paper introduces a new technique to synthesize dexterous manipulation of cloth. Given a simple description of the desired cloth motion, our algorithm computes appropriate joint torques for physically simulated hands, such that, via contact forces, the result of cloth simulation follows the desired motion. Instead of optimizing the hand control forces directly, we formulate an optimization problem that solves for the commanding forces from the hands to the cloth, which have more direct impact on the dynamic state of the hands and that of the cloth. The solution of the optimization provides commanding forces that achieve the desired cloth motion described by the user, while respecting the kinematic constraints of the hands. These commanding forces are then used to guide the joint torques of the hands. To balance between the effectiveness of control and computational costs, we formulate a model-predictive-control problem as a quadratic program at each time step. We demonstrate our technique on a set of cloth manipulation tasks in daily activities, including folding laundry, wringing a towel, and putting on a scarf.
Dexterous Manipulation of Cloth
Ryan Goldade, Christopher Batty, Chris Wojtan
Combining high-resolution level set surface tracking with lower resolution physics is an inexpensive method for achieving highly detailed liquid animations. Unfortunately, the inherent resolution mismatch introduces several types of disturbing visual artifacts. We identify the primary sources of these artifacts and present simple, efficient, and practical solutions to address them. First, we propose an unconditionally stable filtering method that selectively removes sub-grid surface artifacts not seen by the fluid physics, while preserving fine detail in dynamic splashing regions. It provides comparable results to recent error-correction techniques at lower cost, without substepping, and with better scaling behavior. Second, we show how a modified narrow-band scheme can ensure accurate free surface boundary conditions in the presence of large resolution mismatches. Our scheme preserves the efficiency of the narrow-band methodology, while eliminating objectionable stairstep artifacts observed in prior work. Third, we demonstrate that the use of linear interpolation of velocity during advection of the high-resolution level set surface is responsible for visible grid-aligned kinks; we therefore advocate higher-order velocity interpolation, and show that it dramatically reduces this artifact. While these three contributions are orthogonal, our results demonstrate that taken together they efficiently address the dominant sources of visual artifacts arising with high-resolution embedded liquid surfaces; the proposed approach offers improved visual quality, a straightforward implementation, and substantially greater scalability than competing methods.
A Practical Method for High-Resolution Embedded Liquid Surfaces
Eder Miguel, David Miraut, Miguel A. Otaduy
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.
Modeling and Estimation of Energy-Based Hyperelastic Objects
Florian Ferstl, Ryoichi Ando, Chris Wojtan, Rudiger Westermann, Nils Thuerey
The Fluid Implicit Particle method (FLIP) for liquid simulations uses particles to reduce numerical dissipation and provide important visual cues for events like complex splashes and small-scale features near the liquid surface. Unfortunately, FLIP simulations can be computationally expensive, because they require a dense sampling of particles to fill the entire liquid volume. Furthermore, the vast majority of these FLIP particles contribute nothing to the fluid’s visual appearance, especially for larger volumes of liquid. We present a method that only uses FLIP particles within a narrow band of the liquid surface, while efficiently representing the remaining inner volume on a regular grid. We show that a naive realization of this idea introduces unstable and uncontrollable energy fluctuations, and we propose a novel coupling scheme between FLIP particles and regular grid which overcomes this problem. Our method drastically reduces the particle count and simulation times while yielding results that are nearly indistinguishable from regular FLIP simulations. Our approach is easy to integrate into any existing FLIP implementation.
Narrow Band FLIP for Liquid Simulations
Min Tang, Huamin Wang, Le Tang, Ruofeng Tong, Dinesh Manocha
We present a novel GPU-based approach to robustly and efficiently simulate high-resolution and complexly layered cloth. The key component of our formulation is a parallelized matrix assembly algorithm that can quickly build a large and sparse matrix in a compressed format and accurately solve linear systems on GPUs. We also present a fast and integrated solution for parallel collision handling, including collision detection and response computations, which utilizes spatio-temporal coherence. We combine these algorithms as part of a new cloth simulation pipeline that incorporates contact forces into implicit time integration for collision avoidance. The entire pipeline is implemented on GPUs, and we evaluate its performance on complex benchmarks consisting of 100-300K triangles. In practice, our system takes a few seconds to simulate one frame of a complex cloth scene, which represents significant speedups over prior CPU and GPU-based cloth simulation systems.
CAMA: Contact-Aware Matrix Assembly with Unified Collision Handling for GPU-based Cloth Simulation
Eurographics 2016, in Lisbon Portugal, will feature the following physics-related papers:
Christian Dick, Marcus Rogowsky, Rüdiger Westermann
In many numerical simulations of fluids governed by the incompressible Navier-Stokes equations, the pressure Poisson equation needs to be solved to enforce mass conservation. Multigrid solvers show excellent convergence in simple scenarios, yet they can converge slowly in domains where physically separated regions are combined at coarser scales. Moreover, existing multigrid solvers are tailored to specific discretizations of the pressure Poisson equation, and they cannot easily be adapted to other discretizations.
In this paper we analyze the convergence properties of existing multigrid solvers for the pressure Poisson equation in different simulation domains, and we show how to further improve the multigrid convergence rate by using a graph-based extension to determine the coarse grid hierarchy. The proposed multigrid solver is generic in that it can be applied to different kinds of discretizations of the pressure Poisson equation, by using solely the specification of the simulation domain and pre-assembled computational stencils. We analyze the proposed solver in combination with finite difference and finite volume discretizations of the pressure Poisson equation. Our evaluations show that, despite the common assumption, multigrid schemes can exploit their potential even in the most complicated simulation scenarios, yet this behavior is obtained at the price of higher memory consumption.
Solving the Fluid Pressure Poisson Equation Using Multigrid—Evaluation and Improvements
Ben Jones, April Martin, Joshua A. Levine, Tamar Shinar, and Adam W. Bargteil
In this paper, we incorporate ductile fracture into the clustered shape matching simulation framework for deformable bodies, thus filling a gap in the shape matching literature. Our plasticity and fracture models are inspired by the finite element literature on deformable bodies, but are adapted to the clustered shape matching framework. The resulting approach is fast, versatile, and simple to implement.
Ductile Fracture for Clustered Shape Matching
Gerard Pons-Moll, Javier Romero, Naureen Mahmood, and Michael J. Black
To look human, digital full-body avatars need to have soft tissue deformations like those of real people. Current methods for physics simulation of soft tissue lack realism, are computationally expensive, or are hard to tune. Learning soft tissue motion from example, however, has been limited by the lack of dense, high-resolution, training data. We address this using a 4D capture system and a method for accurately registering 3D scans across time to a template mesh. Using over 40,000 scans of ten subjects, we compute how soft tissue motion causes mesh triangles to deform relative to a base 3D body model and learn a low-dimensional linear subspace approximating this soft-tissue deformation. Our model, called Dyna, relates the linear coefficients of this body surface deformation to the changing pose of the body. We learn a second-order auto-regressive model that predicts soft-tissue deformations based on previous deformations, the velocity and acceleration of the body, and the angular velocities and accelerations of the limbs. Dyna also models how deformations vary with a person’s body mass index (BMI), producing different deformations for people with different shapes. Dyna realistically represents the dynamics of soft tissue for previously unseen subjects and motions. Finally, we provide tools for animators to vary BMI to produce different effects, to selectively control the location and intensity of soft-tissue motions, and to apply the model to new, stylized characters.
Dyna: A Model of Dynamic Human Shape in Motion
Prashant Goswami, André Eliasson, Pontus Franzén
This paper presents CUDA-based parallelization of implicit incompressible SPH (IISPH) on the GPU. Along with the detailed exposition of our implementation, we analyze various components involved for their costs. We show that our CUDA version achieves near linear scaling with the number of particles and is faster than the multi-core parallelized IISPH on the CPU. We also present a basic comparison of IISPH with the standard SPH on GPU.
Implicit Incompressible SPH on the GPU