A Smoke Visualization Model for Capturing Surface‐Like Features

Incense, candle smoke and cigarette smoke often exhibit smoke flows with a surface‐like appearance. Although delving into well‐known computational fluid dynamics may provide a solution to create such an appearance, we propose a much efficient alternative that combines a low‐resolution fluid simulation with explicit geometry provided by NURBS surfaces. Among a wide spectrum of fluid simulation, our algorithm specifically tailors to reproduce the semi‐transparent surface look and motion of the smoke. The main idea is that we follow the traces called streaklines created by the advected particles from a simulation and reconstruct NURBS surfaces passing through them. Then, we render the surfaces by applying an opacity map to each surface, where the opacity map is created by utilizing the smoke density and the characteristics of the surface contour. Augmenting the results from low‐resolution simulations such a way requires a low computational cost and memory usage by design.     

Creating and Preserving Vortical Details in SPH Fluid

We present a new method to create and preserve the turbulent details generated around moving objects in SPH fluid. In our approach, a high‐resolution overlapping grid is bounded to each object and translates with the object. The turbulence formation is modeled by resolving the local flow around objects using a hybrid SPH‐FLIP method. Then these vortical details are carried on SPH particles flowing through the local region and preserved in the global field in a synthetic way. Our method provides a physically plausible way to model the turbulent details around both rigid and deformable objects in SPH fluid, and can efficiently produce animations of complex gaseous phenomena with rich visual details.     

Procedural Synthesis using Vortex Particle Method for Fluid Simulation

We propose a fast and effective technique to improve sub‐grid visual details of the grid based fluid simulation. Our method procedurally synthesizes the flow fields coming from the incompressible Navier‐Stokes solver and the vorticity fields generated by vortex particle method for sub‐grid turbulence. We are able to efficiently animate smoke which is highly turbulent and swirling with small scale details. Since this technique does not solve the linear system in high‐resolution grids, it can perform fluid simulation more rapidly. We can easily estimate the influence of turbulent and swirling effect to the fluid flow.     

An Adaptive Contact Model for the Robust Simulation of Knots

In this paper, we present an adaptive model for dynamically deforming hyper‐elastic rods. In contrast to existing approaches, adaptively introduced control points are not governed by geometric subdivision rules. Instead, their states are determined by employing a non‐linear energy‐minimization approach. Since valid control points are computed instantaneously, post‐stabilization schemes are avoided and the stability of the dynamic simulation is improved. Due to inherently complex contact configurations, the simulation of knot tying using rods is a challenging task. In order to address this problem, we combine our adaptive model with a robust and accurate collision handling method for elastic rods. By employing our scheme, complex knot configurations can be simulated in a physically plausible way.     

Large and Small Eddies Matter: Animating Trees in Wind Using Coarse Fluid Simulation and Synthetic Turbulence

Animating trees in wind has long been a problem in computer graphics. Progress on this problem is important for both visual effects in films and forestry biomechanics. More generally, progress on tree motion in wind may inform future work on two‐way coupling between turbulent flows and deformable objects. Synthetic turbulence added to a coarse fluid simulation produces convincing animations of turbulent flows but two‐way coupling between the enriched flow and objects embedded in the flow has not been investigated. Prior work on two‐way coupling between fluid and deformable models lacks a subgrid resolution turbulence model. We produce realistic animations of tree motion by including motion due to both large and small eddies using synthetic subgrid turbulence and porous proxy geometry. Synthetic turbulence at the subgrid scale is modulated using turbulent kinetic energy (TKE). Adding noise after sampling the mean flow and TKE transfers energy from small eddies directly to the tree geometry. The resulting animations include both global sheltering effects and small scale leaf and branch motion. Viewers, on average, found animations, which included both coarse fluid simulation and TKE‐modulated noise to be more accurate than animations generated using coarse fluid simulation or noise alone.     

Single-pass Scalable Subsurface Rendering with Lightcuts

This paper presents a new, scalable, single pass algorithm for computing subsurface scattering using the diffusion approximation. Instead of pre‐computing a globally conservative estimate of the surface irradiance like previous two pass methods, the algorithm simultaneously refines hierarchical and adaptive estimates of both the surface irradiance and the subsurface transport. By using an adaptive, top‐down refinement method, the algorithm directs computational effort only to simulating those eye‐surface‐light paths that make significant contributions to the final image. Because the algorithm is driven by image importance, it scales more efficiently than previous methods that have a linear dependence on translucent surface area. We demonstrate that in scenes with many translucent objects and in complex lighting environments, our new algorithm has a significant performance advantage.     

A Semi-Lagrangian CIP Fluid Solver without Dimensional Splitting

In this paper, we propose a new constrained interpolation profile (CIP) method that is stable and accurate but requires less amount of computation compared to existing CIP‐based solvers. CIP is a high‐order fluid advection solver that can reproduce rich details of fluids. It has third‐order accuracy but its computation is performed over a compact stencil. These advantageous features of CIP are, however, diluted by the following two shortcomings: (1) CIP contains a defect in the utilization of the grid data, which makes the method suitable only for simulations with a tight CFL restriction; and (2) CIP does not guarantee unconditional stability. There have been several attempts to fix these problems in CIP, but they have been only partially successful. The solutions that fixed both problems ended up introducing other undesirable features, namely increased computation time and/or reduced accuracy. This paper proposes a novel modification of the original CIP method that fixes all of the above problems without increasing the computational load or reducing the accuracy. Both quantitative and visual experiments were performed to test the performance of the new CIP in comparison to existing fluid solvers. The results show that the proposed method brings significant improvements in both accuracy and speed.     

Improved Variational Guiding of Smoke Animations

Smoke animations are hard to art‐direct because simple changes in parameters such as simulation resolution often lead to unpredictable changes in the final result. Previous work has addressed this problem with a guiding approach which couples low‐resolution simulations – that exhibit the desired flow and behaviour – to the final, high‐resolution simulation. This is done in such a way that the desired low frequency features are to some extent preserved in the high‐resolution simulation. However, the steady (i.e. constant) guiding used often leads to a lack of sufficiently high detail, and employing time‐dependent guiding is expensive because the matrix of the resulting set of equations needs to be recomputed at every iteration. We propose an improved mathematical model for Eulerian‐based simulations which is better suited for dynamic, time‐dependent guiding of smoke animations through a novel variational coupling of the low‐ and high‐resolution simulations. Our model results in a matrix that does not require re‐computation when the guiding changes over time, and hence we can employ time‐dependent guiding more efficiently both in terms of storage and computational requirements. We demonstrate that time‐dependent guiding allows for more high frequency detail to develop without losing correspondence to the low resolution simulation. Furthermore, we explore various artistic effects made possible by time‐dependent guiding.     

Articulated Object Reconstruction and Markerless Motion Capture from Depth Video

We present an algorithm for acquiring the 3D surface geometry and motion of a dynamic piecewise‐rigid object using a single depth video camera. The algorithm identifies and tracks the rigid components in each frame, while accumulating the geometric information acquired over time, possibly from different viewpoints. The algorithm also reconstructs the dynamic skeleton of the object, thus can be used for markerless motion capture. The acquired model can then be animated to novel poses. We show the results of the algorithm applied to synthetic and real depth video.     

ProcDef: Local‐to‐global Deformation for Skeleton‐free Character Animation

Animations of characters with flexible bodies such as jellyfish, snails, and, hearts are difficult to design using traditional skeleton‐based approaches. A standard approach is keyframing, but adjusting the shape of the flexible body for each key frame is tedious. In addition, the character cannot dynamically adjust its motion to respond to the environment or user input. This paper introduces a new procedural deformation framework (ProcDef) for designing and driving animations of such flexible objects. Our approach is to synthesize global motions procedurally by integrating local deformations. ProcDef provides an efficient design scheme for local deformation patterns; the user can control the orientation and magnitude of local deformations as well as the propagation of deformation signals by specifying line charts and volumetric fields. We also present a fast and robust deformation algorithm based on shape‐matching dynamics and show some example animations to illustrate the feasibility of our framework.     

Mixing Fluids and Granular Materials

Fluid animations in computer graphics show interactions with various kinds of objects. However, fluid flowing through a granular material such as sand is still not possible within current frameworks. In this paper, we present the simulation of fine granular materials interacting with fluids. We propose a unified Smoothed Particle Hydrodynamics framework for the simulation of both fluid and granular material. The granular volume is simulated as a continuous material sampled by particles. By incorporating previous work on porous flow in this simulation framework we are able to fully couple fluid and sand. Fluid can now percolate between sand grains and influence the physical properties of the sand volume. Our method demonstrates various new effects such as dry soil transforming into mud pools by rain or rigid sand structures being eroded by waves.     

Effect of Character Animacy and Preparatory Motion on Perceptual Magnitude of Errors in Ballistic Motion

An increasing number of projects have examined the perceptual magnitude of visible artifacts in animated motion. These studies have been performed using a mix of character types, from detailed human models to abstract geometric objects such as spheres. We explore the extent to which character morphology influences user sensitivity to errors in a fixed set of ballistic motions replicated on three different character types. We find user sensitivity responds to changes in error type or magnitude in a similar manner regardless of character type, but that users display a higher sensitivity to some types of errors when these errors are displayed on more human‐like characters. Further investigation of those error types suggests that being able to observe a period of preparatory motion before the onset of ballistic motion may be important. However, we found no evidence to suggest that a mismatch between the preparatory phase and the resulting ballistic motion was responsible for the higher sensitivity to errors that was observed for the most humanlike character.     

Modal Locomotion: Animating Virtual Characters with Natural Vibrations

We present a general method to intuitively create a wide range of locomotion controllers for 3D legged characters. The key of our approach is the assumption that efficient locomotion can exploit the natural vibration modes of the body, where these modes are related to morphological parameters such as the shape, size, mass, and joint stiffness. The vibration modes are computed for a mechanical model of any 3D character with rigid bones, elastic joints, and additional constraints as desired. A small number of vibration modes can be selected with respect to their relevance to locomotion patterns and combined into a compact controller driven by very few parameters. We show that these controllers can be used in dynamic simulations of simple creatures, and for kinematic animations of more complex creatures of a variety of shapes and sizes.     

Expressive Facial Gestures From Motion Capture Data

Human facial gestures often exhibit such natural stochastic variations as how often the eyes blink, how often the eyebrows and the nose twitch, and how the head moves while speaking. The stochastic movements of facial features are key ingredients for generating convincing facial expressions. Although such small variations have been simulated using noise functions in many graphics applications, modulating noise functions to match natural variations induced from the affective states and the personality of characters is difficult and not intuitive. We present a technique for generating subtle expressive facial gestures (facial expressions and head motion) semi‐automatically from motion capture data. Our approach is based on Markov random fields that are simulated in two levels. In the lower level, the coordinated movements of facial features are captured, parameterized, and transferred to synthetic faces using basis shapes. The upper level represents independent stochastic behavior of facial features. The experimental results show that our system generates expressive facial gestures synchronized with input speech.     

Visualizing Underwater Ocean Optics

Simulating the in‐water ocean light field is a daunting task. Ocean waters are one of the richest participating media, where light interacts not only with water molecules, but with suspended particles and organic matter as well. The concentration of each constituent greatly affects these interactions, resulting in very different hues. Inelastic scattering events such as fluorescence or Raman scattering imply energy transfers that are usually neglected in the simulations. Our contributions in this paper are a bio‐optical model of ocean waters suitable for computer graphics simulations, along with an improved method to obtain an accurate solution of the in‐water light field based on radiative transfer theory. The method provides a link between the inherent optical properties that define the medium and its apparent optical properties, which describe how it looks. The bio‐optical model of the ocean uses published data from oceanography studies. For inelastic scattering we compute all frequency changes at higher and lower energy values, based on the spectral quantum efficiency function of the medium. The results shown prove the usability of the system as a predictive rendering algorithm. Areas of application for this research span from underwater imagery to remote sensing; the resolution method is general enough to be usable in any type of participating medium simulation.     

Embedded Implicit Stand-Ins for Animated Meshes: A Case of Hybrid Modelling

In this paper, we address shape modelling problems, encountered in computer animation and computer games development that are difficult to solve just using polygonal meshes. Our approach is based on a hybrid-modelling concept that combines polygonal meshes with implicit surfaces. A hybrid model consists of an animated polygonal mesh and an approximation of this mesh by a convolution surface stand-in that is embedded within it or is attached to it. The motions of both objects are synchronised using a rigging skeleton. We model the interaction between an animated mesh object and a viscoelastic substance, which is normally represented in an implicit form. Our approach is aimed at achieving verisimilitude rather than physically based simulation. The adhesive behaviour of the viscous object is modelled using geometric blending operations on the corresponding implicit surfaces. Another application of this approach is the creation of metamorphosing implicit surface parts that are attached to an animated mesh. A prototype implementation of the proposed approach and several examples of modelling and animation with near real-time preview times are presented.     

Textured Liquids based on the Marker Level Set

In this work we propose a new Eulerian method for handling the dynamics of a liquid and its surface attributes (for example its color). Our approach is based on a new method for interface advection that we term the Marker Level Set (MLS). The MLS method uses surface markers and a level set for tracking the surface of the liquid, yielding more efficient and accurate results than popular methods like the Particle Level Set method (PLS). Another novelty is that the surface markers allow the MLS to handle non-diffusively surface texture advection, a rare capability in the realm of Eulerian simulation of liquids. We present several simulations of the dynamical evolution of liquids and their surface textures.     

Simulation of Human Motion Data using Short-Horizon Model-Predictive Control

Many data‐driven animation techniques are capable of producing high quality motions of human characters. Few techniques, however, are capable of generating motions that are consistent with physically simulated environments. Physically simulated characters, in contrast, are automatically consistent with the environment, but their motions are often unnatural because they are difficult to control. We present a model‐predictive controller that yields natural motions by guiding simulated humans toward real motion data. During simulation, the predictive component of the controller solves a quadratic program to compute the forces for a short window of time into the future. These forces are then applied by a low‐gain proportional‐derivative component, which makes minor adjustments until the next planning cycle. The controller is fast enough for interactive systems such as games and training simulations. It requires no precomputation and little manual tuning. The controller is resilient to mismatches between the character dynamics and the input motion, which allows it to track motion capture data even where the real dynamics are not known precisely. The same principled formulation can generate natural walks, runs, and jumps in a number of different physically simulated surroundings.     

Deformable Motion: Squeezing into Cluttered Environments

We present an interactive method that allows animated characters to navigate through cluttered environments. Our characters are equipped with a variety of motion skills to clear obstacles, narrow passages, and highly constrained environment features. Our control method incorporates a behavior model into well‐known, standard path planning algorithms. Our behavior model, called deformable motion, consists of a graph of motion capture fragments. The key idea of our approach is to add flexibility on motion fragments such that we can situate them into a cluttered environment via constraint‐based formulation. We demonstrate our deformable motion for realtime interactive navigation and global path planning in highly constrained virtual environments.