SIGGRAPH 2015 Papers

We propose a new method for both collision detection and collision response geared towards handling complex deformable objects in close contact. Our method does not miss collision events between time steps and solves the challenging problem of untangling automatically and robustly. It is conceptually simple and straight forward to parallelize due to the regularity of the algorithm. The main idea is to tessellate the air between objects once before the simulation and by considering one unilateral constraint per element that prevents its inversion during the simulation. If large relative rotations and translations are present in the simulation, an additional dynamic mesh optimization step is needed to prevent mesh locking. This step is fast in 2D and allows the simulation of arbitrary scenes. Because mesh optimization is expensive in 3D, however, the method is best suited for the subclass of 3D scenarios in which relative motions are limited. This subclass contains two important problems, namely the simulation of multi-layered clothing and tissue on animated characters.

We present the first real-time technique to synthesize full-bandwidth sounds for 2D virtual wind instruments. A novel interactive wave solver is proposed that synthesizes audio at 128,000Hz on commodity graphics cards. Simulating the wave equation captures the resonant and radiative properties of the instrument body automatically. We show that a variety of existing non-linear excitation mechanisms such as reed or lips can be successfully coupled to the instrument's 2D wave field. Virtual musical performances can be created by mapping user inputs to control geometric features of the instrument body, such as tone holes, and modifying parameters of the excitation model, such as blowing pressure. Field visualizations are also produced. Our technique promotes experimentation by providing instant audio-visual feedback from interactive virtual designs. To allow artifact-free audio despite dynamic geometric modification, we present a novel time-varying Perfectly Matched Layer formulation that yields smooth, natural-sounding transitions between notes. We find that visco-thermal wall losses are crucial for musical sound in 2D simulations and propose a practical approximation. Weak non-linearity at high amplitudes is incorporated to improve the sound quality of brass instruments.

We present the novel grammar language CGA++ for the procedural modeling of architecture. While existing grammar-based approaches can produce stunning results, they are limited in what modeling scenarios can be realized. In particular, many context-sensitive tasks are precluded, not least because within the rules specifying how one shape is refined, the necessary knowledge about other shapes is not available. Transcending such limitations, CGA++ significantly raises the expressiveness and offers a generic and integrated solution for many advanced procedural modeling problems. Pivotally, CGA++ grants first-class citizenship to shapes, enabling, within a grammar, directly accessing shapes and shape trees, operations on multiple shapes, rewriting shape (sub)trees, and spawning new trees (e.g., to explore multiple alternatives). The new linguistic device of events allows coordination across multiple shapes, featuring powerful dynamic grouping and synchronization. Various examples illustrate CGA++, demonstrating solutions to previously infeasible modeling challenges.

We propose a new adaptive rendering algorithm that enhances the performance of Monte Carlo ray tracing by reducing the noise, i.e., variance, while preserving a variety of high-frequency edges in rendered images through a novel prediction based reconstruction. To achieve our goal, we iteratively build multiple, but sparse linear models. Each linear model has its prediction window, where the linear model predicts the unknown ground truth image that can be generated with an infinite number of samples. Our method recursively estimates prediction errors introduced by linear predictions performed with different prediction windows, and selects an optimal prediction window minimizing the error for each linear model. Since each linear model predicts multiple pixels within its optimal prediction interval, we can construct our linear models only at a sparse set of pixels in the image screen. Predicting multiple pixels with a single linear model poses technical challenges, related to deriving error analysis for regions rather than pixels, and has not been addressed in the field. We address these technical challenges, and our method with robust error analysis leads to a drastically reduced reconstruction time even with higher rendering quality, compared to state-of-the-art adaptive methods. We have demonstrated that our method outperforms previous methods numerically and visually with high performance ray tracing kernels such as OptiX and Embree.

Monte Carlo ray tracing is considered one of the most effective techniques for rendering photo-realistic imagery, but requires a large number of ray samples to produce converged or even visually pleasing images. We develop a novel image-plane adaptive sampling and reconstruction method based on local regression theory. A novel local space estimation process is proposed for employing the local regression, by robustly addressing noisy high-dimensional features. Given the local regression on estimated local space, we provide a novel two-step optimization process for selecting bandwidths of features locally in a data-driven way. Local weighted regression is then applied using the computed bandwidths to produce a smooth image reconstruction with well-preserved details. We derive an error analysis to guide our adaptive sampling process at the local space. We demonstrate that our method produces more accurate and visually pleasing results over the state-of-the-art techniques across a wide range of rendering effects. Our method also allows users to employ an arbitrary set of features, including noisy features, and robustly computes a subset of them by ignoring noisy features and decorrelating them for higher quality.

We describe our successful initiative to accelerate Adobe Illustrator with the graphics hardware pipeline of modern GPUs. Relying on OpenGL 4.4 plus recent OpenGL extensions for advanced blend modes and first-class GPU-accelerated path rendering, we accelerate the Adobe Graphics Model (AGM) layer responsible for rendering sophisticated Illustrator scenes. Illustrator documents render in either an RGB or CMYK color mode. While GPUs are designed and optimized for RGB rendering, we orchestrate OpenGL rendering of vector content in the proper CMYK color space and accommodate the 5+ color components required. We support both non-isolated and isolated transparency groups, knockout, patterns, and arbitrary path clipping. We harness GPU tessellation to shade paths smoothly with gradient meshes. We do all this and render complex Illustrator scenes 2 to 6x faster than CPU rendering at Full HD resolutions; and 5 to 16x faster at Ultra HD resolutions.

We describe a technique to generate imagery with improved sharpness for individuals having refractive vision problems. Our method can reduce their dependence on corrective eyewear. It also benefits individuals with normal vision by improving visual acuity at a distance and of small details. Our approach does not require custom hardware. Instead, the calculated images can be shown on a standard computer display, on printed paper, or superimposed on a physical scene using a projector. Our technique uses a constrained total variation method to produce a deconvolution result which, upon observation, appears sharp at the edges. We introduce a novel relative total variation term that enables controlling ringing reduction, contrast gain, and sharpness. The end result is the ability to generate sharper appearing images, even for individuals with refractive vision problems including myopia, hyperopia, presbyopia, and astigmatism. Our approach has been validated in simulation, in camera-screen experiments, and in a study with human observers.

This paper presents a liquid simulation technique that enforces the incompressibility condition using a stream function solve instead of a pressure projection. Previous methods have used stream function techniques for the simulation of detailed single-phase flows, but a formulation for liquid simulation has proved elusive in part due to the free surface boundary conditions. In this paper, we introduce a stream function approach to liquid simulations with novel boundary conditions for free surfaces, solid obstacles, and solid-fluid coupling. Although our approach increases the dimension of the linear system necessary to enforce incompressibility, it provides interesting and surprising benefits. First, the resulting flow is guaranteed to be divergence-free regardless of the accuracy of the solve. Second, our free-surface boundary conditions guarantee divergence-free motion even in the un-simulated air phase, which enables two-phase flow simulation by only computing a single phase. We implemented this method using a variant of FLIP simulation which only samples particles within a narrow band of the liquid surface, and we illustrate the effectiveness of our method for detailed two-phase flow simulations with complex boundaries, detailed bubble interactions, and two-way solid-fluid coupling.

We present a perceptual control space for simulation of cloth that works with any physical simulator, treating it as a black box. The perceptual control space provides intuitive, art-directable control over the simulation behavior based on a learned mapping from common descriptors for cloth ( e.g. , flowiness, softness) to the parameters of the simulation. To learn the mapping, we perform a series of perceptual experiments in which the simulation parameters are varied and participants assess the values of the common terms of the cloth on a scale. A multi-dimensional sub-space regression is performed on the results to build a perceptual generative model over the simulator parameters. We evaluate the perceptual control space by demonstrating that the generative model does in fact create simulated clothing that is rated by participants as having the expected properties. We also show that this perceptual control space generalizes to garments and motions not in the original experiments.

We present a unified computational approach for taking photos through reflecting or occluding elements such as windows and fences. Rather than capturing a single image, we instruct the user to take a short image sequence while slightly moving the camera. Differences that often exist in the relative position of the background and the obstructing elements from the camera allow us to separate them based on their motions, and to recover the desired background scene as if the visual obstructions were not there. We show results on controlled experiments and many real and practical scenarios, including shooting through reflections, fences, and raindrop-covered windows.