We propose an efficient scheme for evaluating nonlinear subspace forces (and Jacobians) associated with subspace deformations. The core problem we address is efficient integration of the subspace force density over the 3D spatial domain. Similar to Gaussian quadrature schemes that efficiently integrate functions that lie in particular polynomial subspaces, we propose cubature schemes (multi-dimensional quadrature) optimized for efficient integration of force densities associated with particular subspace deformations, particular materials, and particular geometric domains. We support generic subspace deformation kinematics, and nonlinear hyperelastic materials. For an r-dimensional deformation subspace with O(r) cubature points, our method is able to evaluate subspace forces at O(r2) cost. We also describe composite cubature rules for runtime error estimation. Results are provided for various subspace deformation models, several hyperelastic materials (St.Venant-Kirchhoff, Mooney-Rivlin, Arruda-Boyce), and multimodal (graphics, haptics, sound) applications. We show dramatically better efficiency than traditional Monte Carlo integration. CR Categories: I.6.8 [Simulation and Modeling]: Types of Simulation—Animation, I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling—Physically based modeling G.1.4 [Mathematics of Computing]: Numerical Analysis—Quadrature and Numerical Differentiation

We propose an efficient scheme for evaluating nonlinear subspace forces (and Jacobians) associated with subspace deformations. The core problem we address is efficient integration of the subspace force density over the 3D spatial domain. Similar to Gaussian quadrature schemes that efficiently integrate functions that lie in particular polynomial subspaces, we propose cubature schemes (multi-dimensional quadrature) optimized for efficient integration of force densities associated with particular subspace deformations, particular materials, and particular geometric domains. We support generic subspace deformation kinematics, and nonlinear hyperelastic materials. For an r-dimensional deformation subspace with O(r) cubature points, our method is able to evaluate subspace forces at O(r 2 ) cost. We also describe composite cubature rules for runtime error estimation. Results are provided for various subspace deformation models, several hyperelastic materials (St.Venant-Kirchhoff, Mooney-Rivlin, Arruda-Boyce), and multimodal (graphics, haptics, sound) applications. We show dramatically better efficiency than traditional Monte Carlo integration.

Figure 1: Our data-driven approach to synthesizing cloth sounds is able to produce soundtracks for a wide range of common cloth animation scenarios. In this example, the familiar sounds of a windbreaker are synthesized as the character shadow boxes. AbstractWe present a practical data-driven method for automatically synthesizing plausible soundtracks for physics-based cloth animations running at graphics rates. Given a cloth animation, we analyze the deformations and use motion events to drive crumpling and friction sound models estimated from cloth measurements. We synthesize a low-quality sound signal, which is then used as a target signal for a concatenative sound synthesis (CSS) process. CSS selects a sequence of microsound units, very short segments, from a database of recorded cloth sounds, which best match the synthesized target sound in a low-dimensional feature-space after applying a handtuned warping function. The selected microsound units are concatenated together to produce the final cloth sound with minimal filtering. Our approach avoids expensive physics-based synthesis of cloth sound, instead relying on cloth recordings and our motiondriven CSS approach for realism. We demonstrate its effectiveness on a variety of cloth animations involving various materials and character motions, including first-person virtual clothing with binaural sound.

We propose and evaluate a method for significantly compressing modal sound models, thereby making them far more practical for audiovisual applications. The dense eigenmode matrix, needed to compute the sound model's response to contact forces, can consume tens to thousands of megabytes depending on mesh resolution and mode count. Our eigenmode compression pipeline is based on nonlinear optimization of Moving Least Squares (MLS) approximations. Enhanced compression is achieved by exploiting symmetry both within and between eigenmodes, and by adaptively assigning per-mode error levels based on human perception of the far-field pressure amplitudes. Our method provides smooth eigenmode approximations, and efficient random access. We demonstrate that, in many cases, hundredfold compression ratios can be achieved without audible degradation of the rendered sound.

Figure 1: Our data-driven approach to synthesizing cloth sounds is able to produce soundtracks for a wide range of common cloth animation scenarios. In this example, the familiar sounds of a windbreaker are synthesized as the character shadow boxes. AbstractWe present a practical data-driven method for automatically synthesizing plausible soundtracks for physics-based cloth animations running at graphics rates. Given a cloth animation, we analyze the deformations and use motion events to drive crumpling and friction sound models estimated from cloth measurements. We synthesize a low-quality sound signal, which is then used as a target signal for a concatenative sound synthesis (CSS) process. CSS selects a sequence of microsound units, very short segments, from a database of recorded cloth sounds, which best match the synthesized target sound in a low-dimensional feature-space after applying a handtuned warping function. The selected microsound units are concatenated together to produce the final cloth sound with minimal filtering. Our approach avoids expensive physics-based synthesis of cloth sound, instead relying on cloth recordings and our motiondriven CSS approach for realism. We demonstrate its effectiveness on a variety of cloth animations involving various materials and character motions, including first-person virtual clothing with binaural sound.

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Figure 1: Crash! Our physically based sound renderings of thin shells produce characteristic "crashing" and "rumbling" sounds when animated using rigid body dynamics. We synthesize nonlinear modal vibrations using an efficient reduced-order dynamics model that captures important nonlinear mode coupling. High-resolution sound field approximations are generated using far-field acoustic transfer (FFAT) maps, which are precomputed using efficient fast Helmholtz multipole methods, and provide cheap evaluation of detailed low-to high-frequency acoustic transfer functions for realistic sound rendering. AbstractWe propose a procedural method for synthesizing realistic sounds due to nonlinear thin-shell vibrations. We use linear modal analysis to generate a small-deformation displacement basis, then couple the modes together using nonlinear thin-shell forces. To enable audiorate time-stepping of mode amplitudes with mesh-independent cost, we propose a reduced-order dynamics model based on a thin-shell cubature scheme. Limitations such as mode locking and pitch glide are addressed. To support fast evaluation of mid-frequency modebased sound radiation for detailed meshes, we propose far-field acoustic transfer maps (FFAT maps) which can be precomputed using state-of-the-art fast Helmholtz multipole methods. Familiar examples are presented including rumbling trash cans and plastic bottles, crashing cymbals, and noisy sheet metal objects, each with increased richness over linear modal sound models.