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.

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Figure 1: A dynamic "virtual stuntman" falls to the ground, rolls over, and rises to an erect position, balancing in gravity. AbstractAn 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 stuntperson. Our composition framework promises to enable the community of physics-based animation practitioners to easily exchange motor controllers and integrate them into dynamic characters.

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.

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In this paper we propose a general framework for local pathplanning and steering that can be easily extended to perform highlevel 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 spacetime 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 highlevel responses to never seen before situations.

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.

Figure 1: Snapshots of a bottleneck and a doorway scenario, showing progress from left to right. The hybrid approach efficiently handles large crowds and challenging space-time scenarios. AbstractNext-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 5× 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.