Agent-based synthetic crowd simulation affords the cost-effective large-scale simulation and animation of interacting digital humans. Model-based approaches have successfully generated a plethora of simulators with a variety of foundations. However, prior approaches have been based on statically defined models predicated on simplifying assumptions, limited video-based datasets, or homogeneous policies. Recent works have applied reinforcement learning to learn policies for navigation. However, these approaches may learn static homogeneous rules, are typically limited in their generalization to trained scenarios, and limited in their usability in synthetic crowd domains. In this article, we present a multi-agent reinforcement learning-based approach that learns a parametric predictive collision avoidance and steering policy. We show that training over a parameter space produces a flexible model across crowd configurations. That is, our goal-conditioned approach learns a parametric policy that affords heterogeneous synthetic crowds. We propose a model-free approach without centralization of internal agent information, control signals, or agent communication. The model is extensively evaluated. The results show policy generalization across unseen scenarios, agent parameters, and out-of-distribution parameterizations. The learned model has comparable computational performance to traditional methods. Qualitatively the model produces both expected (laminar flow, shuffling, bottleneck) and unexpected (side-stepping) emergent qualitative behaviours, and quantitatively the approach is performant across measures of movement quality.

The visualization of spaces, both virtual and built, has long been an important part of the environment design process. Industry tools to visualize occupancy have grown from simple drop-in stock photos post-design to real-time crowds simulations. However, while treatment of visualization and collaborative design processes has long been discussed in the HCI and Architecture communities, these inclusive design methods are infrequently seen in architecture education (e.g. studio) and practice, nor implemented in licensure requirements – leaving designers to think about the future occupants on their own. While there are strong indicators of the impact visualization modality and rendering style have on perception of scale and space, little has been explored regarding how we represent the human form with respect to these design tools and practices. We present findings from a novel online interactive space planning and estimation study that examines the effects of 3 common building visualization modalities in the design process with 3 human form modalities extracted from the architecture literature. Results indicate the type of visualization changes the number of occupants estimated, and that designers prefer integrated manikins within building models when estimating space usage, although their acceptance was equally divided between 2D and 3D. Our findings lay the foundation for new and focused design tools integrating human form and factors at building scale.

Modeling visual attention is an important aspect of simulating realistic virtual humans. This work proposes a parametric model and method for generating real-time saliency maps from the perspective of virtual agents which approximate those of vision-based saliency approaches. The model aggregates a saliency score from user-defined parameters for objects and characters in an agent’s view and uses that to output a 2D saliency map which can be modulated by an attention field to incorporate 3D information as well as a character’s state of attentiveness. The aggregate and parameterized structure of the method allows the user to model a range of diverse agents. The user may also expand the model with additional layers and parameters. The proposed method can be combined with normative and pathological models of the human visual field and gaze controllers, such as the recently proposed model of egocentric distractions for casual pedestrians that we use in our results.

Abstract At present, environment designers mostly use their intuition and experience to predictively account for how environments might support dynamic activity. The majority of Computer‐Aided Design tools only provide a static representation of space which potentially ignores the impact that an environment layout produces on its occupants and their movements. To address this, computational techniques such as crowd simulation have been developed. With few exceptions, crowd simulation frameworks are often decoupled from environment modeling tools. They usually require specific hardware/software infrastructures and expertise to be used, hindering the designers' abilities to seamlessly simulate, analyze, and incorporate movement‐centric dynamics into their design workflows. To bridge this disconnect, we devise a cross‐browser service‐based simulation analytics platform to analyze environment layouts with respect to occupancy and activity. Our platform allows users to access simulation services by uploading three‐dimensional environment models in numerous common formats, devise targeted simulation scenarios, run simulations, and instantly generate crowd‐based analytics for their designs. We conducted a case study to showcase cross‐domain applicability of our service‐based platform, and a user study to evaluate the usability of this approach.

In architectural design, architects explore a vast amount of design options to maximize various performance criteria, while adhering to specific constraints. In an effort to assist architects in such a complex endeavour, we propose IDOME, an interactive system for computer-aided design optimization. Our approach balances automation and control by efficiently exploring, analyzing, and filtering space layouts to inform architects' decision-making better. At each design iteration, IDOME provides a set of alternative building layouts which satisfy user-defined constraints and optimality criteria concerning a user-defined space parametrization. When the user selects a design generated by IDOME, the system performs a similar optimization process with the same (or different) parameters and objectives. A user may iterate this exploration process as many times as needed. In this work, we focus on optimizing built environments using architectural metrics by improving the degree of visibility, accessibility, and information gaining for navigating a proposed space. This approach, however, can be extended to support other kinds of analysis as well. We demonstrate the capabilities of IDOME through a series of examples, performance analysis, user studies, and a usability test. The results indicate that IDOME successfully optimizes the proposed designs concerning the chosen metrics and offers a satisfactory experience for users with minimal training.

Many multi-agent navigation approaches make use of simplified representations such as a disk. These simplifications allow for fast simulation of thousands of agents but limit the simulation accuracy and fidelity. In this paper, we propose a fully integrated physical character control and multi-agent navigation method. In place of sample complex online planning methods, we extend the use of recent deep reinforcement learning techniques. This extension improves on multi-agent navigation models and simulated humanoids by combining Multi-Agent and Hierarchical Reinforcement Learning. We train a single short term goal-conditioned low-level policy to provide directed walking behaviour. This task-agnostic controller can be shared by higher-level policies that perform longer-term planning. The proposed approach produces reciprocal collision avoidance, robust navigation, and emergent crowd behaviours. Furthermore, it offers several key affordances not previously possible in multi-agent navigation including tunable character morphology and physically accurate interactions with agents and the environment. Our results show that the proposed method outperforms prior methods across environments and tasks, as well as, performing well in terms of zero-shot generalization over different numbers of agents and computation time.

The use of mobile devices is one of the most commonly observed family of distracted behaviours exhibited by pedestrians in urban environments. We develop an event-driven behaviour tree model for distracted pedestrians that includes initiating mobile device use as well as terminating or pausing mobile device use based on internal or external cues to refocus attention. We present a simple, probabilistic attention model for such pedestrians. The proposed model is not meant to be complete. It primarily focuses on computing the probability that a distracted agent looks up, based on the agent’s individual characteristics and the elements in their environment. We condition the potentially attention grabbing elements in the environment on distraction-specific egocentric fields for visual attention. We also propose an oriented ellipse model for capturing the affects of cognitively fuzzy goals during distracted navigation. Our model is simple and intuitively parameterized, and thus can be easily edited and extended.

The importance of social distancing for public health is well established. However, the policies and regulations regarding occupancy rates have not been designed with this in mind. While there are analytical tools and related measures that are used in practice to evaluate how the design of a built environment serves the needs of its intended occupants, these metrics cannot directly apply to the problem of preventing the spread of infectious diseases such as COVID-19. By using a crowd-based simulator using three levels of behavior and agent control in a given environment, a novel evaluation metric for a space layout can be calculated to reflect the proclivity of maintaining a safe distance throughout the shopping experience. We refer to this metric as the Social Distancing Index (SDI), accounting for the occupancy throughput and number of distance-based violations found. Through a case study of a realistic retail store, we demonstrate the proposed platforms performance and output on multiple scenarios by changing agent-behavior, occupancy rate, and navigational guidelines.

Crowd simulations provide a practical approach to evaluate building design alternatives with respect to human-centric criteria, such as evacuation times and flow in case of emergency scenarios. Coupled with Building Information Modeling (BIM) tools, they support architects’ iterative exploration of design alternatives. However, methods based on manually configuring a design and a corresponding simulation are not practical for exploring the potentially very large number of design solutions that satisfy human-centric design goals and requirements. Often, for practical reasons, designers may consider standard crowd configurations which do not capture the behavior of diverse occupants that may exhibit different locomotion abilities, movement patterns, and social behaviors. We posit that a joint exploration of high-dimensional building design and occupancy features is necessary to more accurately capture the mutual relations between buildings and the behavior of their occupants. To test this hypothesis, we conducted a series of experiments to automatically explore joint high dimensional design–occupancy patterns using an unsupervised pattern recognition technique (i.e. K-MEANS). We demonstrate that joint design–occupancy explorations provide more accurate results compared with sequential exploration processes that consider default design or crowd features, despite the longer computational times to simulate a large number of solutions. The findings of this case study have practical applications to the design of next-generation design exploration tools that support human-centric analyses in architectural design.

Abstract Authoring behavior narratives for heterogeneous multiagent virtual humans engaged in collaborative, localized, and task‐based behaviors can be challenging. Traditional behavior authoring frameworks are either space‐centric, where occupancy parameters are specified; behavior‐centric, where multiagent behaviors are defined; or agent‐centric, where desires and intentions drive agents' behavior. In this paper, we propose to integrate these approaches into a unique framework to author behavior narratives that progressively satisfy time‐varying building‐level occupancy specifications, room‐level behavior distributions, and agent‐level motivations using a prioritized resource allocation system. This approach can generate progressively more complex and plausible narratives that satisfy spatial, behavioral, and social constraints. Possible applications of this system involve computer gaming and decision‐making in engineering and architectural design.

We present a computational spatial analytics tool for designing environments that better support human-related factors. Our system performs both static and dynamic analyses: the first relates to the building geometry and organization, while the second additionally considers the crowd movement in the space. The results are presented to the designers in the form of numerical values, traces and heat maps displayed on top of the floor plan. We demonstrate our approach with a user study whereby novice architects have tested the proposed approach to iteratively improve a building accessibility in real-time with respect to a selected number of static and dynamic metrics. The results indicate that the users were able to successfully improve their design solutions and thus generate more human-aware environments. The usability and effectiveness of the tool where also measured, yielding positive scores. The modular and flexible nature of the tool enables further extension to incorporate additional static and dynamic spatial metrics.

Floor plan designs and their spatial analysis are typically constrained to blueprints and 2D projections of 3D models. Computing appropriate spatial measures from such representations provides a standard way of quantifying important aspects of the design. We wish to investigate whether a person's perceptual exploration of a space would agree with such spatial measures, that is, whether a person can roughly infer such measures by exploring a space. We perform two studies, one involving novices and the other experts. First, we conduct a perceptual study to discover whether a novice user's perception of spatial measures depends on the mode used to explore the space. Our analysis considers three spatial measures, grounded in Space-Syntax, that characterize key aspects of a design such as visibility, accessibility, and organization. We compare three modes of exploration: 2D blueprints, first-person view in a 3D simulation, and a 3D virtual reality simulation with teleportation. A correlation analysis between the users' perceptual ratings and the spatial measures, indicates that virtual reality is the most effective of the three methods, while 2D blueprints and 3D first-person exploration often fail entirely to convey the spatial measures. In the second study, experts are asked to evaluate and rank the design blueprints for each measure. The expert observations are in strong agreement with the spatial measures for accessibility and organization, but not for visibility in some cases. This indicates that even experts have difficulty understanding spatial aspects of an architecture design from 2D blueprints alone.

Crowd-aware environment design is a complex combinatorial decision process, where small changes in a design may affect crowd flow patterns in unexpected and potentially unintuitive ways. Existing solutions rely on expert intuition, best practices, or automation. To address the dimensionality and complexity of the design process, we propose leveraging automation and human creativity at a large scale akin to crowd sourcing, within a gamified collaborative design framework. Using our system, "players" (novice users or experts) can rapidly iterate on their designs while soliciting feedback from computer simulations of crowd movement and the designs of other players. Our approach affords a new way of thinking of the solution space in that it inherently supports competitive collaboration, co-design, and crowd sourced solutions. We evaluate our framework through a preliminary user study.

Crowd simulations facilitate the study of how an environment layout impacts the movement and behavior of its inhabitants. However, simulations are computationally expensive, which make them infeasible when used as part of interactive systems (e.g., Computer-Assisted Design software). Machine learning models, such as neural networks (NN), can learn observed behaviors from examples, and can potentially offer a rational prediction of a crowd's behavior efficiently. To this end, we propose a method to predict the aggregate characteristics of crowd dynamics using regression neural networks (NN). We parametrize the environment, the crowd distribution and the steering method to serve as inputs to the NN models, while a number of common performance measures serve as the output. Our preliminary experiments show that our approach can help users evaluate a large number of environments efficiently.

Abstract Traffic and pedestrian dynamics communities often use a standard qualitative classification, namely, level of service (LoS), to describe the relationship between the crowd flow and crowd density in an environment. However, this classification has not yet been rigorously studied in the application of synthetic crowds, which are derived using a variety of approaches and may model certain behaviors better than others. Although synthetic crowds can be simulated to extrapolate crowd flow for rigorous quantitative analysis, these may be at odds with the qualitative LoS. In order to successfully use computer‐assisted design, it is important to have sound quantitative metrics as the basis for analysis and optimization. In this paper, we present a systematic empirical analysis of LoS for synthetic crowds. Using established crowd simulation techniques, we quantify the relation between crowd density and crowd flow for evacuation scenarios across different simulators to explore conformity to qualitative LoS classifications. Following this study, we perform environment optimization experiments under various LoS conditions. Finally, we test the generality of optimizing under these LoS conditions. Our results motivate the need for further study, using real and synthetic crowd datasets across representative environment benchmarks.

Abstract We present crowd‐optimized design of environments (CODE): a “crowd‐aware” computational tool for designing environments (e.g., building floor plans). Our system analyses the impact of newly added environment elements (e.g., pillars or doorways) on the resulting crowd flow, using current‐generation crowd simulators. The results of the simulation are used to provide feedback to the designer in terms of aggregate statistics and heat maps. Additionally, our system is able to “automatically” optimize the placement of environment elements to maximize crowd flow in egress scenarios, while satisfying constraints that are imposed by the designer. Using CODE, architects and environment designers can iteratively refine upon their original design to quickly accommodate the dynamic properties of crowd simulations in an interactive fashion. CODE is modular and flexible so that designers may build environments, select from different crowd simulators, and specify varying crowd configurations.

Abstract The proposed method computes a navigation mesh for arbitrary and dynamic 3D environments based on curvature and is robust and efficient. This method addresses a number of known limitations in state‐of‐the‐art techniques to produce navigation meshes that are tightly coupled to the original geometry, incorporate geometric details that are crucial for movement decisions, can robustly handle complex surfaces and can efficiently repair the navigation mesh to accommodate dynamically changing environments. The method is integrated into a standard navigation and collision avoidance system to simulate thousands of agents on complex 3D surfaces in real time. Copyright © 2016 John Wiley & Sons, Ltd.

We propose a method to robustly and efficiently compute a navigation mesh for arbitrary and dynamic 3D environments based on curvature. This method addresses a number of known limitations in state-of-the-art techniques to produce navigation meshes that are tightly coupled to the original geometry, incorporate geometric details that are crucial for movement decisions and robustly handle complex surfaces. We integrate the method into a standard navigation and collision-avoidance system to simulate thousands of agents on complex 3D surfaces in real-time.

Level of service (LoS) is a standard indicator, widely used in crowd management and urban design, for characterizing the service afforded by environments to crowds of specific densities. However, current LoS indicators are qualitative and rely on expert analysis. Computational approaches for crowd analysis and environment design require robust measures for characterizing the relationship between environments and crowd flow.

Abstract The layout of a building, real or virtual, affects the flow patterns of its intended users. It is well established, for example, that the placement of pillars at proper locations can often facilitate pedestrian flow during the evacuation of a building. Such considerations are therefore important for architects, game level developers, and others whose domains involve agents navigating through buildings. In this paper, we take the first steps towards developing a simulation framework that can be used to study the optimal placement of architectural elements, such as pillars or doors, for the purposes of facilitating dense pedestrian flow during the evacuation of a building. In particular, we show that the steering algorithms used to model the local navigation abilities of the agents significantly affect the results, which motivates the need for a statistically valid approach and further study. Copyright © 2015 John Wiley & Sons, Ltd.

Balancing the interactions between game level design and intended player experience is a difficult and time consuming process. Automating aspects of this process with respect to user-defined constraints has beneficial implications for game designers. A change in level layout may affect the available routes and subsequent player interactions for a number of agents within the level. Small changes in the placement of game elements may lead to significant changes in terms of the challenge experienced by the player on the path to their goal. Estimating the effect of this change requires that the designer take into account new paths of all interacting agents and how these may affect the player. As the number of these agents grow to crowd size, estimating the effect of these changes becomes grows difficult. We present a user-in-the-loop framework for tackling this task by optimizing enemy agent settings and the placement of game elements that affect the flow of agents within the level, with respect to estimated difficulty. Using static path analysis we estimate difficulty based on agent interactions with the player. To exemplify the usefulness of the framework, we show that small changes in level layout lead to significant changes in game difficulty, and optimizations with respect to the characterization of difficulty can be used to attain desired difficulty levels.

The complexity of interactive virtual worlds has increased dramatically in recent years, with a rise in mature solutions for designing large-scale environments and populating them with hundreds and thousands of autonomous characters. An interesting problem that arises in this context, and that has received little attention to date, is whether we can predict the complexity of a steering scenario by analyzing the configuration of the environment and the agents involved. We statically analyze an input scenario and compute a set of novel salient features which characterize the expected interactions between agents and obstacles during simulation. Using a statistical approach, we automatically derive the relative influence of each feature on the complexity of a scenario in order to derive a single numerical quantity of expected scenario complexity. We validate our proposed metric by demonstrating a strong negative correlation between the statically computed expected complexity and the dynamic performance of three published crowd simulation techniques.

Precise control with proportional-derivative (PD) control generally requires stiffness. The proposed method determines critically damped PD control trajectories that precisely obtain target position and velocity constraints for arbitrary initial conditions. An analytic solution provides the PD control parameters, thereby determining the required impedance. The resulting controller precisely interpolates the target state by solving the full boundary-value problem. Control parameters are time-invariant, and need only be recomputed if the system diverges from the computed trajectory due to unexpected forces or noise. The resulting method provides control with automatically determined compliance, yielding natural response to perturbation.

There has been growing academic and industry interest in the behavioral animation of autonomous actors in virtual worlds. However, it remains a considerable challenge to automatically generate complicated interactions between multiple actors in a customizable way with minimal user specification.

Next-generation steering algorithms will need to support thousands of believable individual agents, capable of steering in very challenging situations with low-latency reactions. In this paper we propose a steering framework that offers three key contributions: (a) It integrates several models of steering into a single steering decision, (b) it employs a novel space-time planning approach to allow agents to steer during complex local interactions, and (c) it varies the frequency of update of each component (phase) of the framework to drastically improve performance. We demonstrate the versatility and robustness of our framework using a large number of test cases. We also show that the frequency of updates for each phase of the framework can be "decimated" by a surprisingly large amount before resulting steering behaviors degrade. This technique achieves more than a 5x performance improvement, allowing the use of better, more costly algorithms for robust steering, while supporting thousands of agents with low-latency reactions in real-time.

The majority of previous crowd 'steering algorithms model each character as an oriented particle that moves by choosing a force or velocity vector. In many cases, orientation is heuristically chosen to be the same as the particle's velocity. This approach has the two key disadvantages:

Abstract The majority of steering algorithms output only a force or velocity vector to an animation system, without modeling the constraints and capabilities of human‐like movement. This simplistic approach lacks control over how a character should navigate. This paper proposes a steering method that usesfootstepsto navigate characters in dynamic crowds. Instead of an oriented particle with a single collision radius, we model a character's center of mass and footsteps using a 2D approximation of an inverted spherical pendulum model of bipedal locomotion. We use this model to generate a timed sequence of footsteps that existing animation techniques can follow exactly. Our approach not only constrains characters to navigate with realistic steps but also enables characters to intelligently control subtlenavigationbehaviors that are possible with exact footsteps, such as side‐stepping. Our approach can navigate crowds of hundreds of individual characters with collision‐free, natural steering decisions in real‐time. Copyright © 2011 John Wiley & Sons, Ltd.

Abstract We present a simple and intuitive method for encapsulating part of agents' steering and coordinating abilities into a new class of agents, called situation agents. Situation agents have all the abilities of typical agents. In addition, they can influence the steering decisions of any agent, including other situation agents, within their sphere of influence. Encapsulating steering logic into moving agents is a powerful abstraction which provides more flexibility and efficiency than traditional informed environment approaches, and works with many of the current steering methodologies. We demonstrate our proposed approach in a number of challenging scenarios. Copyright © 2010 John Wiley & Sons, Ltd.

Fluid bipedal locomotion remains a significant challenge for humanoid robotics. Recent bio-inspired approaches have made significant progress by using small numbers of tightly coupled neurons, called central pattern generators (CPGs). Our approach exchanges complexity of the neuron model for complexity of the network, gradually building a network of simple neurons capable of complex behaviors. We show this approach generates controllersde novothat are able to control 3D bipedal locomotion up to 10 meters. This results holds for robots with human-proportionate morphologies across 95% of normal human variation. The resulting networks are then examined to discover neural structures that arise unusually often, lending some insight into the workings of otherwise opaque controllers.

In this paper we propose a general framework for local path-planning and steering that can be easily extended to perform high-level behaviors. Our framework is based on the concept of affordances - the possible ways an agent can interact with its environment. Each agent perceives the environment through a set of vector and scalar fields that are represented in the agent's local space. This egocentric property allows us to efficiently compute a local space-time plan. We then use these perception fields to compute a fitness measure for every possible action, known as an affordance field. The action that has the optimal value in the affordance field is the agent's steering decision. Using our framework, we demonstrate autonomous virtual pedestrians that perform steering and path planning in unknown environments along with the emergence of high-level responses to never seen before situations.

Abstract Steering is a challenging task, required by nearly all agents in virtual worlds. There is a large and growing number of approaches for steering, and it is becoming increasingly important to ask a fundamental question: how can we objectively compare steering algorithms? To our knowledge, there is no standard way of evaluating or comparing the quality of steering solutions. This paper presents SteerBench: a benchmark framework for objectively evaluating steering behaviors for virtual agents. We propose a diverse set of test cases, metrics of evaluation, and a scoring method that can be used to compare different steering algorithms. Our framework can be easily customized by a user to evaluate specific behaviors and new test cases. We demonstrate our benchmark process on two example steering algorithms, showing the insight gained from our metrics. We hope that this framework can grow into a standard for steering evaluation. Copyright © 2009 John Wiley & Sons, Ltd.

The error tolerance of human perception offers a range of opportunities to trade numerical accuracy for performance in physics-based simulation. However, most prior work on perceptual error tolerance either focus exclusively on understanding the tolerance of the human visual system or burden the application developer with case-specific implementations such as Level-of-Detail (LOD) techniques. In this article, based on a detailed set of perceptual metrics, we propose a methodology to identify the maximum error tolerance of physics simulation. Then, we apply this methodology in the evaluation of four case studies. First, we utilize the methodology in the tuning of the simulation timestep. The second study deals with tuning the iteration count for the LCP solver. Then, we evaluate the perceptual quality of Fast Estimation with Error Control (FEEC) [Yeh et al. 2006]. Finally, we explore the hardware optimization technique of precision reduction.

Future interactive entertainment applications will featurethe physical simulation of thousands of interacting objectsusing explosions, breakable objects, and cloth effects. Whilethese applications require a tremendous amount of performanceto satisfy the minimum frame rate of 30 FPS, there is a dramatic amount of parallelism in future physics workloads.How will future physics architectures leverage parallelismto achieve the real-time constraint?.

Physics-based animation has enormous potential to improve the realism of interactive entertainment through dynamic, immersive content creation. Despite the massively parallel nature of physics simulation, fully exploiting this parallelism to reach interactive frame rates will require significant area to place the large number of cores. Fortunately, interactive entertainment requires believability rather than accuracy. Recent work shows that real-time physics has a remarkable tolerance for reduced precision of the significant in floating-point (FP) operations. In this paper, we describe an architecture with a hierarchical floating-point unit (FPU) that leverages dynamic precision reduction to enable efficient FPU sharing among multiple cores. This sharing reduces the area required by these cores, thereby allowing more cores to be packed into a given area and exploiting more parallelism.

Editing recorded motions to make them suitable for different sets of environmental constraints is a general and difficult open problem. In this paper we solve a significant part of this problem by modifying full-body motions with an interactive randomized motion planner. Our method is able to synthesize collision-free motions for specified linkages of multiple animated characters in synchrony with the characters' full-body motions. The proposed method runs at interactive speed for dynamic environments of realistic complexity. We demonstrate the effectiveness of our interactive motion editing approach with two important applications: (a) motion correction (to remove collisions) and (b) synthesis of realistic object manipulation sequences on top of locomotion.

We propose a precomputation-based approach for the real-time rendering of scenes that include a number of complex illumination phenomena, such as radiosity and subsurface scattering, and allows interactive modification of camera and lighting parameters. At the heart of our approach lies a novel parameterization of the rendering equation that is inherently supported by the modern GPU. During the pre-computation phase, we build a set of offset transfer maps based on the proposed parameterization, which approximate the complete radiance transfer function for the scene. The rendering phase is then reduced to a set of texture-blending and mapping operations that execute in real-time on the GPU. In contrast to the current state-of-the-art, which employs environment maps to produce global illumination, our approach uses arbitrary first-order lighting to compute a final lighting solution, and fully supports point and spot lights. To discretize the transfer maps, we develop an efficient method for generating and sampling C0-continuous probability density functions from unordered data points.We believe that the contributions of this paper offer a significantly different approach to precomputed radiance transfer from those previously proposed.

Speech-driven facial motion synthesis is a well explored research topic. However, little has been done to model expressive visual behavior during speech. We address this issue using a machine learning approach that relies on a database of speech-related high-fidelity facial motions. From this training set, we derive a generative model of expressive facial motion that incorporates emotion control, while maintaining accurate lip-synching. The emotional content of the input speech can be manually specified by the user or automatically extracted from the audio signal using a Support Vector Machine classifier.

We present a framework for composing motor controllers into autonomous composite reactive behaviors for bipedal robots and autonomous, physically-simulated humanoids. A key contribution of our composition framework is an explicit model of the "pre-conditions" under which motor controllers are expected to function properly. Pre-conditions may be determined manually or learned automatically by algorithms based on support vector machine (SVM) learning theory. We demonstrate controller composition and evaluate our composition framework using a family of controllers capable of synthesizing basic actions such a balance, protective stepping when balance is disturbed, protective arm reactions when falling, and multiple ways of regaining an upright stance after a fall.

We implement a framework for animating interactive characters by combining kinematic animation with physical simulation. The combination of animation techniques allows the characters to exploit the advantages of each technique. For example, characters can perform natural-looking kinematic gaits and react dynamically to unexpected situations. Kinematic techniques such as those based on motion capture data can create very natural-looking animation. However, motion capture based techniques are not suitable for modeling the complex interactions between dynamically interacting characters. Physical simulation, on the other hand, is well suited for such tasks. Our work develops kinematic and dynamic controllers and transition methods between the two control methods for interactive character animation. In addition, we utilize the motion graph technique to develop complex kinematic animation from shorter motion clips as a method of kinematic control.

No abstract available.

We study and characterize the topology of the Internet at the autonomous system (AS) level. First, we show that the topology can be described efficiently with power laws. The elegance and simplicity of the power laws provide a novel perspective into the seemingly uncontrolled Internet structure. Second, we show that power laws have appeared consistently over the last five years. We also observe that the power laws hold even in the most recent and more complete topology with correlation coefficient above 99% for the degree-based power law. In addition, we study the evolution of the power-law exponents over the five-year interval and observe a variation for the degree-based power law of less than 10%. Thirdly, we provide relationships between the exponents and other topological metrics.

An ambitious goal in the area of physics-based computer animation is the creation of virtual actors that autonomously synthesize realistic human motions and possess a broad repertoire of lifelike motor skills. To this end, the control of dynamic, anthropomorphic figures subject to gravity and contact forces remains a difficult open problem. We propose a framework for composing controllers in order to enhance the motor abilities of such figures. A key contribution of our composition framework is an explicit model of the “pre-conditions” under which motor controllers are expected to function properly. We demonstrate controller composition with pre-conditions determined not only manually, but also automatically based on Support Vector Machine (SVM) learning theory. We evaluate our composition framework using a family of controllers capable of synthesizing basic actions such as balance, protective stepping when balance is disturbed, protective arm reactions when falling, and multiple ways of standing up after a fall. We furthermore demonstrate these basic controllers working in conjunction with more dynamic motor skills within a prototype virtual stunt-person. Our composition framework promises to enable the community of physics-based animation practitioners to easily exchange motor controllers and integrate them into dynamic characters.

Dynamic simulation is a potentially useful tool for creating realistic motion for animated characters. However, improved control techniques are required before this approach will bear fruit. We compare and contrast control for animation with control for robotics. This is followed by an overview of two control methods which were conceived in the context of computer animation, but which also have potential applications for robotic control.

Despite the apparent randomness of the Internet, we discover some surprisingly simple power-laws of the Internet topology. These power-laws hold for three snapshots of the Internet, between November 1997 and December 1998, despite a 45% growth of its size during that period. We show that our power-laws fit the real data very well resulting in correlation coefficients of 96% or higher.Our observations provide a novel perspective of the structure of the Internet. The power-laws describe concisely skewed distributions of graph properties such as the node outdegree. In addition, these power-laws can be used to estimate important parameters such as the average neighborhood size, and facilitate the design and the performance analysis of protocols. Furthermore, we can use them to generate and select realistic topologies for simulation purposes.

Free form deformations (FFDs) are a popular tool for modeling and keyframe animation. The paper extends the use of FFDs to a dynamic setting. Our goal is to enable normally inanimate graphics objects, such as teapots and tables, to become animated, and learn to move about in a charming, cartoon like manner. To achieve this goal, we implement a system that can transform a wide class of objects into dynamic characters. Our formulation is based on parameterized hierarchical FFDs augmented with Lagrangian dynamics, and provides an efficient way to animate and control the simulated characters. Objects are assigned mass distributions and elastic deformation properties, which allow them to translate, rotate, and deform according to internal and external forces. In addition, we implement an automated optimization process that searches for suitable control strategies. The primary contributions of the work are threefold. First, we formulate a dynamic generalization of conventional, geometric FFDs. The formulation employs deformation modes which are tailored by the user and are expressed in terms of FFDs. Second, the formulation accommodates a hierarchy of dynamic FFDs that can be used to model local as well as global deformations. Third, the deformation modes can be active, thereby producing locomotion.