Modeling Stochastic Dynamical Systems for Interactive Simulation

We present techniques for constructing approximate stochastic models of complicated dynamical systems for applications in interactive computer graphics. The models are designed to produce realistic interaction at low cost.
We describe two kinds of stochastic models: continuous state (ARX) models and discrete state (Markov chains) models. System identi cation techniques are used for learning the input-output dynamics automatically, from either measurements of a real system or from an accurate simulation. The synthesis of behavior in this manner is several orders of magnitude faster than physical simulation.We demonstrate the techniques with two examples: (1) the dynamics of candle ame in the wind, modeled using data from a real candle and (2) the motion of a falling leaf, modeled using data from a complex simulation. We have implemented an interactive Java program which demonstrates real-time interaction with a realistically behaving simulation of a cartoon candle ame. The user makes the ame animation icker by blowing into a microphone.     

Adaptive Nonlinear Finite Elements for Deformable Body Simulation Using Dynamic Progressive Meshes

Realistic behavior of deformable objects is essential for many applications such as simulation for surgical training. Existing techniques of deformable modeling for real time simulation have either used approximate methods that are not physically accurate or linear methods that do not produce reasonable global behavior. Nonlinear finite element methods (FEM) are globally accurate, but conventional FEM is not real time. In this paper, we apply nonlinear FEM using mass lumping to produce a diagonal mass matrix that allows real time computation. Adaptive meshing is necessary to provide sufficient detail where required while minimizing unnecessary computation. We propose a scheme for mesh adaptation based on an extension of the progressive mesh concept, which we call dynamic progressive meshes.     

A High Performance Solver for the Animation of Deformable Objects using Advanced Numerical Methods

Physically based modelling of deformable objects has become the most popular technique to model textiles, skin or human tissue. The crucial problem in the animation of deformable objects is the solution of the resulting differential equations. Recently fast solutions have been presented. In this work we will first give a theoretical analysis and then exploit special properties of the system and advanced numerical techniques to achieve further speed-ups of the simulations. Also, higher accuracy, leading to higher quality animations, will be achieved and an error bound is enforced.     

Generalized Stochastic Sampling Method for Visualization and Investigation of Implicit Surfaces

Recently we proposed the stochastic sampling method (SSM), which can numerically generate sample points on complicated implicit surfaces quickly and uniformly. In this paper we generalize the method in two aspects: (1) We introduce two kinds of boundary conditions, so that we can sample a finite part of an open surface spreading infinitely. (2) We generalize the stochastic differential equation used in the SSM, so that its solutions can satisfy plural constraint conditions simultaneously. The first generalization enables us to visualize cut views of open surfaces. The second generalization enables us to visualize intersections of static and moving implicit surfaces, which leads to detailed investigation of intersections and other interesting applications such as visualization of contour maps.     

Integrating Behavioural Animation Techniques

Our research focuses on animating autonomous virtual humans which are able to take decisions by themselves. We especially address in this paper the technical problem of integrating altogether the physical simulation of agents (represented as virtual humans in a 3D environment) and their behaviours and motivations, driven by a Beliefs, Desires and Intentions architecture. We also explain how goals drive plans, and how an agent can coherently handle concurrent tasks.     

Modeling Dynamic Hair as a Continuum

In this paper we address the difficult problem of hair dynamics, particularly hair-hair and hair-air interactions. To model these interactions, we propose to consider hair volume as a continuum. Subsequently, we treat the interaction dynamics to be fluid dynamics. This proves to be a strong as well as viable approach for an otherwise very complex phenomenon. However, we retain the individual character of hair, which is vital to visually realistic rendering of hair animation. For that, we develop an elaborate model for stiffness and inertial dynamics of individual hair strand. Being a reduced coordinate formulation, the stiffness dynamics is numerically stable and fast. We then unify the continuum interaction dynamics and the individual hair's stiffness dynamics.     

A survey of research work in computer science and cognitive science dedicated to the modeling of reactive human behaviors

Modeling believable autonomous agents needs to take into account many different aspects from very different disciplines, ranging from cognitive psychology to mechanics. In this paper, we focus on research work dedicated to the modeling of human decision in a reactive way, a domain in‐between the biomechanical motion control of the activity and the rational and social background which motivates and shapes the execution of such activities. We cover models of reactive human behaviors introduced in computer science and cognitive science, assessing and comparing them from the application‐oriented perspective of modeling credible real‐time virtual anthropomorphic actors. Copyright © 2010 John Wiley & Sons, Ltd.     

A framework for interactive responsive animation

The availability of high‐performance 3D workstations has increased the range of application for interactive real‐time animation. In these applications the user can directly interact with the objects in the animation and direct the evolution of their motion, rather than simply watching a pre‐computed animation sequence. Interactive real‐time animation has fast‐growing applications in virtual reality, scientific visualization, medical training and distant learning. Traditional approaches to computer animation have been based on the animator having complete control over all aspects of the motion. In interactive animation the user can interact with any of the objects, which changes the current motion path or behaviour in real time. The objects in the animation must be capable of reacting to the user's actions and not simply replay a canned motion sequence. This paper presents a framework for interactive animation that allows the animator to specify the reactions of objects to events generated by other objects and the user. This framework is based on the concept of relations that describe how an object reacts to the influence of a dynamic environment. Each relation specifies one motion primitive triggered by either its enabling condition or the state of the environment. A collection of the relations is structured through several hierarchical layers to produce responsive behaviours and their variations. This framework is illustrated by several room‐based dancing examples that are modelled by relations. Copyright © 2000 John Wiley & Sons, Ltd.     

Hierarchical Impostors for the Flocking Algorithm in 3D

The availability of powerful and affordable 3D PC graphics boards has made the rendering of rich immersiveenvironments possible at interactive speeds. The scene update rate and the appropriate behaviour of objects withinthe world are central to this immersive feeling. This paper is concerned with the behaviour computations involvedin the flocking algorithm, which has been used extensively to emulate the flocking behaviour of creatures found innature. The main contribution of this paper is a new method for hierarchically combining portions of the flocksinto groups to reduce the cost of the behavioural computation, allowing far larger flocks to be updated in real‐timein the world. ACM CSS: I.3.7 Three‐Dimensional Graphics and Realism—Animation     

Constructing Human Motion Manifold With Sequential Networks

This paper presents a novel recurrent neural network-based method to construct a latent motion manifold that can represent a wide range of human motions in a long sequence. We introduce several new components to increase the spatial and temporal coverage in motion space while retaining the details of motion capture data. These include new regularization terms for the motion manifold, combination of two complementary decoders for predicting joint rotations and joint velocities and the addition of the forward kinematics layer to consider both joint rotation and position errors. In addition, we propose a set of loss terms that improve the overall quality of the motion manifold from various aspects, such as the capability of reconstructing not only the motion but also the latent manifold vector, and the naturalness of the motion through adversarial loss. These components contribute to creating compact and versatile motion manifold that allows for creating new motions by performing random sampling and algebraic operations, such as interpolation and analogy, in the latent motion manifold.     

一种基于自由空间法的虚拟人行走规划方法

本文提出了一种基于自由空间法的虚拟人行走规划方法。该方法以虚拟环境的表示为基础,首先将虚拟环境离散化成为环境图;然后用启发式A＊搜索算法进行路径搜索,产生从初始位置到目标位置的最优路径,引导虚拟人对环境进行漫游;最后设计实现了一个仿真演示实例。仿真结果表明,所提算法简便易行,能够满足虚拟人在复杂环境下导
航和漫游的要求。     

Detection and Synthesis of Full-Body Environment Interactions for Virtual Humans

We present a new methodology for enabling virtual humans to autonomously detect and perform complex full-body interactions with their environments. Given a parameterized walking controller and a set of motion-captured example interactions, our method is able to detect when interactions can occur and to coordinate the detected upper-body interaction with the walking controller in order to achieve full-body mobile interactions in similar situations. Our approach is based on learning spatial coordination features from the example motions and on associating body-environment proximity information to the body configurations of each performed action. Body configurations become the input to a regression system, which in turn is able to generate new interactions for different situations in similar environments. The regression model is capable of selecting, encoding and replicating key spatial strategies with respect to body coordination and management of environment constraints as well as determining the correct moment in time and space for starting an interaction. As a result, we obtain an interactive controller able to detect and synthesize coordinated full-body motions for a variety of complex interactions requiring body mobility. Our results achieve complex interactions, such as opening doors and drawing in a wide whiteboard. The presented approach introduces the concept of learning interaction coordination models that can be applied on top of any given walking controller. The obtained method is simple and flexible, it handles the detection of possible interactions and is suitable for real-time applications.     

Stellar Mesh Simplification Using Probabilistic Optimization

This paper proposes the stellar mesh simplification method, a fast implementation of the Four‐Face Cluster (FFC) algorithm. In this method, a probabilistic optimization heuristic substitutes the priority queue of the original method, which results in a 40% faster algorithm with the same order of distortion. It extends naturally to a progressive and/or multiresolution scheme for combinatorial surfaces. This work also presents a simple way to encode the hierarchy of the resulting multiresolution meshes. This work also focuses on important aspects for the development of a practical and robust implementation of this simplification technique, and on the analysis of the influence of the parameters.     

Animation modeling with petri nets

This paper introduces the use of Petri Nets as a modeling and analysis tool for animation environments. Firstly, the original formulation for Petri Nets is applied in two animation situations, one modeled as a state machine and another exploring interdependent transitions. Increasing the complexity level, some modeling extensions are discussed and more sophisticated animation examples are studied.     

A Flexible Architecture for Virtual Humans in Networked Collaborative Virtual Environments

Complex virtual human representation provides more natural interaction and communication among participants in networked virtual environments, hence it is expected to increase the sense of being together within the same virtual world. We present a flexible framework for the integration of virtual humans in networked collaborative virtual environments. A modular architecture allows flexible representation and control of the virtual humans, whether they are controlled by a physical user using all sorts of tracking and other devices, or by an intelligent control program turning them into autonomous actors. The modularity of the system allows for fairly easy extensions and integration with new techniques making it interesting also as a testbed for various domains from “classic” VR to psychological experiments. We present results in terms of functionalities, example applications and measurements of performance and network traffic with an increasing number of participants in the simulation.     

Modelling selective visual attention for autonomous virtual characters

Autonomous virtual characters (AVCs) are becoming more prevalent both for real‐time interaction and also as digital actors in film and TV production. AVCs require believable virtual human animations, accompanied by natural attention generation, and thus the software that controls the AVCs needs to model when and how to interact with the objects and other characters that exist in the virtual environment. This paper models automatic attention behaviour using a saliency model that generates plausible targets for combined gaze and head motions. The model was compared with the default behaviour of the Second Life (SL) system in an object observation scenario while it was compared with real actors' behaviour in a conversation scenario. Results from a study run within the SL system demonstrate a promising attention model that is not just believable and realistic but also adaptable to varying task, without any prior knowledge of the virtual scene. Copyright © 2011 John Wiley & Sons, Ltd.     

Detail‐Preserving Explicit Mesh Projection and Topology Matching for Particle‐Based Fluids

We propose a new explicit surface tracking approach for particle‐based fluid simulations. Our goal is to advect and update a highly detailed surface, while only computing a coarse simulation. Current explicit surface methods lose surface details when projecting on the isosurface of an implicit function built from particles. Our approach uses a detail‐preserving projection, based on a signed distance field, to prevent the divergence of the explicit surface without losing its initial details. Furthermore, we introduce a novel topology matching stage that corrects the topology of the explicit surface based on the topology of an implicit function. To that end, we introduce an optimization approach to update our explicit mesh signed distance field before remeshing. Our approach is successfully used to preserve the surface details of melting and highly viscous objects, and shown to be stable by handling complex cases involving multiple topological changes. Compared to the computation of a high‐resolution simulation, using our approach with a coarse fluid simulation significantly reduces the computation time and improves the quality of the resulting surface.     

A Constraint-Based Reasoning Framework for Behavioural Animation

Behaviour is a reflection of a reasoning process that must deal with constraints imposed by an external environment, internal knowledge and physical structure. This paper proposes a framework for behavioural animation that is based on the next generation of object-oriented, constraint-based expert systems technology, and applies a control structure of knowledge agents and knowledge units to determine the behaviour of objects to be animated. Knowledge agents are responsible for planning, plan implementation and information extraction from the environment. The activity of an agent is dependent on the knowledge units ascribed to them by the animator. The interaction between agents and knowledge units is resolved by the reasoning engine, and thus, influences the eventual motion displayed. An example given is NSAIL, a pilot implementation using the model-based ECHIDNA constraint logic programming shell. With this approach, the motion for a sailing scenario and other behavioural domains can be specified at a high level through the characterization of the knowledge agents.