Theodore Kim

Wavelet turbulence for fluid simulation

We present a novel wavelet method for the simulation of fluids at high spatial resolution. The algorithm enables large- and small-scale detail to be edited separately, allowing high-resolution detail to be added as a post-processing step. Instead of solving the Navier-Stokes equations over a highly refined mesh, we use the wavelet decomposition of a low-resolution simulation to determine the location and energy characteristics of missing high-frequency components. We then synthesize these missing components using a novel incompressible turbulence function, and provide a method to maintain the temporal coherence of the resulting structures. There is no linear system to solve, so the method parallelizes trivially and requires only a few auxiliary arrays. The method guarantees that the new frequencies will not interfere with existing frequencies, allowing animators to set up a low resolution simulation quickly and later add details without changing the overall fluid motion.

Optimizing cubature for efficient integration of subspace deformations

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 sub-space 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 multi-modal (graphics, haptics, sound) applications. We show dramatically better efficiency than traditional Monte Carlo integration.

Feature-guided dynamic texture synthesis on continuous flows

We present a technique for synthesizing spatially and temporally varying textures on continuous flows using image or video input, guided by the physical characteristics of the fluid stream itself. This approach enables the generation of realistic textures on the fluid that correspond to the local flow behavior, creating the appearance of complex surface effects, such as foam and small bubbles. Our technique requires only a simple specification of texture behavior, and automatically generates and tracks the features and texture over time in a temporally coherent manner. Based on this framework, we also introduce a technique to perform feature-guided video synthesis. We demonstrate our algorithm on several simulated and recorded natural phenomena, including splashing water and lava flows. We also show how our methodology can be extended beyond realistic appearance synthesis to more general scenarios, such as temperature-guided synthesis of complex surface phenomena in a liquid during boiling.

A hybrid algorithm for modeling ice formation

We present a novel algorithm that simulates ice formation. Motivated by the physical process of ice growth, we develop a novel hybrid algorithm by synthesizing three techniques: diffusion limited aggregation, phase field methods, and stable fluid solvers. Each technique maps to one of the three stages of solidification. The visual realism of the resulting algorithm appears to surpass that of each technique alone, particularly in animations of freezing. In addition, we present a faster, simplified phase field method, as well as a unified parameterization that enables artistic manipulation of the simulation. We illustrate the results on arbitrary 3D surfaces.

Simulating articulated subspace self-contact

We present an efficient new subspace method for simulating the self-contact of articulated deformable bodies, such as characters. Self-contact is highly structured in this setting, as the limited space of possible articulations produces a predictable set of coherent collisions. Subspace methods can leverage this coherence, and have been used in the past to accelerate the collision detection stage of contact simulation. We show that these methods can be used to accelerate the entire contact computation, and allow self-contact to be resolved without looking at all of the contact points. Our analysis of the problem yields a broader insight into the types of non-linearities that subspace methods can efficiently approximate, and leads us to design a pose-space cubature scheme. Our algorithm accelerates self-contact by up to an order of magnitude over other subspace simulations, and accelerates the overall simulation by two orders of magnitude over full-rank simulations. We demonstrate the simulation of high resolution (100K -- 400K elements) meshes in self-contact at interactive rates (5.8 -- 50 FPS).

Closest point turbulence for liquid surfaces

We propose a method of increasing the apparent spatial resolution of an existing liquid simulation. Previous approaches to this “up-resing” problem have focused on increasing the turbulence of the underlying velocity field. Motivated by measurements in the free surface turbulence literature, we observe that past certain frequencies, it is sufficient to perform a wave simulation directly on the liquid surface, and construct a reduced-dimensional surface-only simulation. We sidestep the considerable problem of generating a surface parameterization by employing an embedding technique known as the Closest Point Method (CPM) that operates directly on a 3D extension field. The CPM requires 3D operators, and we show that for surface operators with no natural 3D generalization, it is possible to construct a viable operator using the inverse Abel transform. We additionally propose a fast, frozen core closest point transform, and an advection method for the extension field that reduces smearing considerably. Finally, we propose two turbulence coupling methods that seed the high-resolution wave simulation in visually expected regions.

Hardware-aware analysis and optimization of stable fluids

We perform a detailed flop and bandwidth analysis of Jos Stams Stable Fluids algorithm on the CPU, GPU, and Cell. In all three cases, we find that the algorithm is bandwidth bound, with the cores sitting idle up to 96% of the time. Knowing this, we propose two modifications to accelerate the algorithm. First, a Mehrstellen discretization for the pressure solver which reduces the running time of the solver by a third. Second, a static caching scheme that eliminates roughly 99% of the random lookups in the advection stage. We observe a 2x speedup in the advection stage using this scheme. Both modifications apply equally well to all three architectures.

A simple boiling module

Recent efforts to visually capture the phenomena of boiling have proposed monolithic approaches that extend the basic techniques underlying existing fluid solvers. In this work, we show that if we instead treat boiling as a separate computational module to be loosely coupled to an existing solver, a very easy to implement, highly efficient algorithm can be designed that produces excellent visual results, even on coarse (643) grids. The algorithm is also highly SIMD-amenable, allowing the boiling computation to be farmed out to a GPU or Playstation 3 Cell processor. Our algorithm takes less than 100 lines of commented, readable C + +, and can be integrated into an existing particle level set fluid solver with virtually no modifications. A serial implementation consumes between 3-5% of the overall running time, and a preliminary SIMD implementation shows that a 643 simulation runs at 130 FPS, making the computational cost of the module totally negligible.

Surface turbulence for particle-based liquid simulations

We present a method to increase the apparent resolution of particle-based liquid simulations. Our method first outputs a dense, temporally coherent, regularized point set from a coarse particle-based liquid simulation. We then apply a surface-only Lagrangian wave simulation to this high-resolution point set. We develop novel methods for seeding and simulating waves over surface points, and use them to generate high-resolution details. We avoid error-prone surface mesh processing, and robustly propagate waves without the need for explicit connectivity information. Our seeding strategy combines a robust curvature evaluation with multiple bands of seeding oscillators, injects waves with arbitrarily fine-scale structures, and properly handles obstacle boundaries. We generate detailed fluid surfaces from coarse simulations as an independent post-process that can be applied to most particle-based fluid solvers.

Subspace condensation: full space adaptivity for subspace deformations

Subspace deformable body simulations can be very fast, but can behave unrealistically when behaviors outside the prescribed subspace such as novel external collisions, are encountered. We address this limitation by presenting a fast, flexible new method that allows full space computation to be activated in the neighborhood of novel events while the rest of the body still computes in a subspace. We achieve this using a method we call subspace condensation, a variant on the classic static condensation precomputation. However, instead of a precomputation, we use the speed of subspace methods to perform the condensation at every frame. This approach allows the full space regions to be specified arbitrarily at runtime, and forms a natural two-way coupling with the subspace regions. While condensation is usually only applicable to linear materials, the speed of our technique enables its application to non-linear materials as well. We show the effectiveness of our approach by applying it to a variety of articulated character scenarios.