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.

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.

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.

Recently we proposed an efficient technique based on analysis-based sparsity in tight frames to restore images affected by shift-invariant blur and additive white Gaussian noise. Here we apply the same alternate marginal optimization idea used in that work, but dealing with combined Poissonian-Gaussian noise, which we approximate as Gaussian, additive and signal-dependent. We operate (1) by adding to the original image and its associated sparse vector another auxiliary variable to be optimized in the loop: the blurred, but not yet noisy, image; and (2) by using a quadratic soft constraint. We also re-formulate the previous prior modelling in order to perform a standard maximum a posteriori estimation, and generalize the approach to allow for a multi-frame prior. The proposed technique is computationally efficient and yields state-of-the-art performance.

Recently we proposed an efficient technique based on analysis-based sparsity in tight frames to restore images affected by linear blur and additive white Gaussian noise. Such technique performed a joint optimization of a cost function in the image and coefficient space by alternating their corresponding marginal minimizations, until convergence. Here we propose, first, a more standard Bayesian (MAP) re-formulation of the model/method, yielding the same cost function. Second, we propose a heuristical twist in the method, consisting of running only a few iterations (too few to reach convergence), with a dynamic shrinkage plan obtained by maximizing a performance measure in a set of deblurring tests. Third, we propose a multi-frame prior scheme allowing for different thresholds in different frames. Compared to the original method, we achieve a very important speed-up, while keeping high robustness and state-of-the-art performance.