Figure 1: Our method uses a precomputed dataset to synthesize detailed cloth wrinkles (a) that are layered onto a coarse base simulation (inset). The precomputed datset can be used to synthesize wrinkles for a wide range of poses (b and c). AbstractThis paper describes a method for animating the appearance of clothing, such as pants or a shirt, that fits closely to a figure's body. Compared to flowing cloth, such as loose dresses or capes, these types of garments involve nearly continuous collision contact and small wrinkles, that can be troublesome for traditional cloth simulation methods. Based on the observation that the wrinkles in closefitting clothing behave in a predominantly kinematic fashion, we have developed an example-based wrinkle synthesis technique. Our method drives wrinkle generation from the pose of the figure's kinematic skeleton. This approach allows high quality clothing wrinkles to be combined with a coarse cloth simulation that computes the global and dynamic aspects of the clothing motion. While the combined results do not exactly match a high-resolution reference simulation, they do capture many of the characteristic fine-scale features and wrinkles. Further, the combined system runs at interactive rates, making it suitable for applications where high-resolution offline simulations would not be a viable option. The wrinkle synthesis method uses a precomputed database built by simulating the high-resolution clothing as the articulated figure is moved over a range of poses. In principle, the space of poses is exponential in the total number of degrees of freedom; however clothing wrinkles are primarily affected by the nearest joints, allowing each joint to be processed independently. During synthesis, mesh interpolation is used to consider the influence of multiple joints, and combined with a coarse simulation to produce the final results at interactive rates.

In this paper, we study the use of the Chebyshev semi-iterative approach in projective and position-based dynamics. Although projective dynamics is fundamentally nonlinear, its convergence behavior is similar to that of an iterative method solving a linear system. Because of that, we can estimate the "spectral radius" and use it in the Chebyshev approach to accelerate the convergence by at least one order of magnitude, when the global step is handled by the direct solver, the Jacobi solver, or even the Gauss-Seidel solver. Our experiment shows that the combination of the Chebyshev approach and the direct solver runs fastest on CPU, while the combination of the Chebyshev approach and the Jacobi solver outperforms any other combination on GPU, as it is highly compatible with parallel computing. Our experiment further shows position-based dynamics can be accelerated by the Chebyshev approach as well, although the effect is less obvious for tetrahedral meshes. The whole approach is simple, fast, effective, GPU-friendly, and has a small memory cost.

Cloth often has complicated nonlinear, anisotropic elastic behavior due to its woven pattern and fiber properties. However, most current cloth simulation techniques simply use linear and isotropic elastic models with manually selected stiffness parameters. Such simple simulations do not allow differentiating the behavior of distinct cloth materials such as silk or denim, and they cannot model most materials with fidelity to their real-world counterparts. In this paper, we present a data-driven technique to more realistically animate cloth. We propose a piecewise linear elastic model that is a good approximation to nonlinear, anisotropic stretching and bending behaviors of various materials. We develop new measurement techniques for studying the elastic deformations for both stretching and bending in real cloth samples. Our setup is easy and inexpensive to construct, and the parameters of our model can be fit to observed data with a well-posed optimization procedure. We have measured a database of ten different cloth materials, each of which exhibits distinctive elastic behaviors. These measurements can be used in most cloth simulation systems to create natural and realistic clothing wrinkles and shapes, for a range of different materials.

Figure 1: Benchmark Andy: Our GPU-based approach can simulate the clothes dressed on a Kung-Fu boy. The meshes of three cloth pieces are represented by 127K triangles. Our simulator performs all of the computations, including implicit time integration and collision handling, in 2.42s per frame (on average) on an NVIDIA Telsa K40c GPU. Our new parallel algorithms for sparse matrix assembly and collision handling result in significant speedups over prior methods. AbstractWe 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. † {tang_m, tangle,

We show that many existing elastic body simulation approaches can be interpreted as descent methods, under a nonlinear optimization framework derived from implicit time integration. The key question is how to find an effective descent direction with a low computational cost. Based on this concept, we propose a new gradient descent method using Jacobi preconditioning and Chebyshev acceleration. The convergence rate of this method is comparable to that of L-BFGS or nonlinear conjugate gradient. But unlike other methods, it requires no dot product operation, making it suitable for GPU implementation. To further improve its convergence and performance, we develop a series of step length adjustment, initialization, and invertible model conversion techniques, all of which are compatible with GPU acceleration. Our experiment shows that the resulting simulator is simple, fast, scalable, memory-efficient, and robust against very large time steps and deformations. It can correctly simulate the deformation behaviors of many elastic materials, as long as their energy functions are second-order differentiable and their Hessian matrices can be quickly evaluated. For additional speedups, the method can also serve as a complement to other techniques, such as multi-grid.