Painting with Turbulence

Inspired by a study that identified a strong similarity between Vincent van Gogh\u27s and Jackson Pollock\u27s painting techniques, this thesis explores the interplay between science and art, specifically the unpredictable behaviors in turbulent flows and aesthetic concepts in painting. It utilizes data from a GPU-based air flow simulation, and presents a framework for artists to visualize the chaotic property changes in turbulent flows and create paintings with turbulence data. While the creation of individual brushstrokes is procedural and driven by simulation, artists are able to exercise their aesthetic judgments at various stages during a painting creation. A short animation demonstrates the potential results from this framework

Visual Simulation of Flow

We have adopted a numerical method from computational fluid dynamics, the Lattice Boltzmann Method (LBM), for real-time simulation and visualization of flow and amorphous phenomena, such as clouds, smoke, fire, haze, dust, radioactive plumes, and air-borne biological or chemical agents. Unlike other approaches, LBM discretizes the micro-physics of local interactions and can handle very complex boundary conditions, such as deep urban canyons, curved walls, indoors, and dynamic boundaries of moving objects. Due to its discrete nature, LBM lends itself to multi-resolution approaches, and its computational pattern, which is similar to cellular automata, is easily parallelizable. We have accelerated LBM on commodity graphics processing units (GPUs), achieving real-time or even accelerated real-time on a single GPU or on a GPU cluster. We have implemented a 3D urban navigation system and applied it in New York City with real-time live sensor data. In addition to a pivotal application in simulation of airborne contaminants in urban environments, this approach will enable the development of other superior prediction simulation capabilities, computer graphics and games, and a novel technology for computational science and engineering

Screen space animation of fire

International audienceWe present a simple and physically inspired method to animate realistically looking fire directly in 2D instead of along a 3D simulation. This naturally reduces the complexity of the animation from O(n3) to O(n2). The fire is represented as a 2D scalar density field located on a plane facing the camera, and is advected under a 2.5D velocity field. In our method, the apparent motion of the fire on the viewing axis is mimicked by introducing vibrations in the velocity field. We model these rapid vibrations as pressure waves found in compressible fluids and therefore consider the full Navier-Stokes equations. The equations can be solved in a single pass and our method entirely runs on the GPU. A natural extension is to make use of this method directly in screen space: instead of filtering down the fire's simulation grid in world space, we rasterize the fire's source, and perform the simulation on a coarser grid directly in screen space. The results are constantly renewed 3D-looking fires computed solely in 2D

Implementation and Applications of Art-directable Ocean Simulation Tools

Ocean effects are important aspects of the filmmaking. They help to establish emotions and dynamism via the behaviors of the oceans, and provides the different atmosphere for storytelling by creating various ocean scenarios. Gilligan is a prototype environmental scene simulator, whose core technique of the ocean simulation has been widely used in feature film productions. This thesis develops ocean simulation tools working with Maya and Houdini with the techniques provided by Gilligan. The Gilligan-Maya workflow executes ocean simulation methods in Gilligan to simulate oceans. The Gilligan-Houdini workflow integrates Gilligan into Houdini, containing a Houdini wrapper of Gilligan, along with a series of Houdini digital assets to support the usage. Artists can use these tools to generate ocean effects, with controls to simplify the production workflow, and well-exposed to all the simulation data for advanced development. This thesis demonstrates several applications with different ocean effects scenarios: ocean environment creation, ocean with floating objects, and ocean character effects

Transport-Based Neural Style Transfer for Smoke Simulations

Artistically controlling fluids has always been a challenging task. Optimization techniques rely on approximating simulation states towards target velocity or density field configurations, which are often handcrafted by artists to indirectly control smoke dynamics. Patch synthesis techniques transfer image textures or simulation features to a target flow field. However, these are either limited to adding structural patterns or augmenting coarse flows with turbulent structures, and hence cannot capture the full spectrum of different styles and semantically complex structures. In this paper, we propose the first Transport-based Neural Style Transfer (TNST) algorithm for volumetric smoke data. Our method is able to transfer features from natural images to smoke simulations, enabling general content-aware manipulations ranging from simple patterns to intricate motifs. The proposed algorithm is physically inspired, since it computes the density transport from a source input smoke to a desired target configuration. Our transport-based approach allows direct control over the divergence of the stylization velocity field by optimizing incompressible and irrotational potentials that transport smoke towards stylization. Temporal consistency is ensured by transporting and aligning subsequent stylized velocities, and 3D reconstructions are computed by seamlessly merging stylizations from different camera viewpoints.Comment: ACM Transaction on Graphics (SIGGRAPH ASIA 2019), additional materials: http://www.byungsoo.me/project/neural-flow-styl

Statistical and Directable Methods for Large-Scale Rigid Body Simulation

This dissertation describes several techniques to improve performance and controllability of large-scale rigid body simulations. We first describe a statistical simulation method that replaces certain stages of rigid body simulation with a statistically- based approximation. We begin by collecting statistical data regarding changes in linear and angular momentum for collisions of a given object. From the data, we extract a statistical ”signature” for the object, giving a compact representation of the object’s response to collision events. During object simulation, both the collision detection and the collision response calculations are replaced by simpler calculations based on the statistical signature. In addition, based on our statistical simulator, we develop a mixed rigid body simulator that combines an impulse-based with a statistically-based collision response method. This allows us to maintain high accuracy in important parts of the scene while achieving greater efficiency by simplifying less important parts of the simulation. The resulting system gives speedups of more than an order of magnitude on several large rigid body simulations while maintaining high accuracy in key places and capturing overall statistical behavior in other places. Also, we introduce two methods for directing pile behavior to form the desired shapes. To fill up the space inside the desired shapes and maintain the stability of the desired pile shapes, our methods analyze the configurations and status of all objects and properly select some candidates to have their degrees of freedom (DOFs) reduced. Our first method utilizes the idea of angles of repose to perform the analysis. According to the desired angle of repose, we create an additional spatial structure to track the piling status and select suitable objects to reduce their DOFs. In our second method, we adapt equilibrium analysis in a local scheme to find “stable” objects of the stacking structure. Then, we restrict their DOFs by adding constraints on them for stabilizing the structure. Overall, our directing methods generate a wider variety of piled structures than possible with strict physically-based simulation

Real-time Physics Based Simulation for 3D Computer Graphics

Restoration of realistic animation is a critical part in the area of computer graphics. The goal of this sort of simulation is to imitate the behavior of the transformation in real life to the greatest extent. Physics-based simulation provides a solid background and proficient theories that can be applied in the simulation. In this dissertation, I will present real-time simulations which are physics-based in the area of terrain deformation and ship oscillations. When ground vehicles navigate on soft terrains such as sand, snow and mud, they often leave distinctive tracks. The realistic simulation of such vehicle-terrain interaction is important for ground based visual simulations and many video games. However, the existing research in terrain deformation has not addressed this issue effectively. In this dissertation, I present a new terrain deformation algorithm for simulating vehicle-terrain interaction in real time. The algorithm is based on the classic terramechanics theories, and calculates terrain deformation according to the vehicle load, velocity, tire size, and soil concentration. As a result, this algorithm can simulate different vehicle tracks on different types of terrains with different vehicle properties. I demonstrate my algorithm by vehicle tracks on soft terrain. In the field of ship oscillation simulation, I propose a new method for simulating ship motions in waves. Although there have been plenty of previous work on physics based fluid-solid simulation, most of these methods are not suitable for real-time applications. In particular, few methods are designed specifically for simulating ship motion in waves. My method is based on physics theories of ship motion, but with necessary simplifications to ensure real-time performance. My results show that this method is well suited to simulate sophisticated ship motions in real time applications