Petros Faloutsos

On power-law relationships of the Internet topology

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.

Power laws and the AS-level Internet topology

In this paper, 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%. Third, we provide relationships between the exponents and other topological metrics.

Composable controllers for physics-based character animation

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 free-form deformations for animation synthesis

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.

Expressive speech-driven facial animation

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.

The Art of Deception: Adaptive Precision Reduction for Area Efficient Physics Acceleration

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.

Egocentric affordance fields in pedestrian steering

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 agents 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 agents 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.

Hybrid control for interactive character animation

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.

The virtual stuntman: dynamic characters with a repertoire of autonomous motor skills

Abstract 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. In this paper, we report on our ongoing development of a virtual stuntman, a dynamic graphical character that possesses a nontrivial repertoire of lifelike motor skills. The repertoire includes basic actions such as balance, protective stepping when balance is disturbed, protective arm reactions when falling, multiple ways of rising upright after a fall, and several more vigorously dynamic motor skills. Our virtual stuntman is the product of a recently proposed framework for integrating motor controllers, which includes among other ingredients an explicit model of pre-conditions; i.e., those regions of a dynamic figures state space within which a given motor controller is applicable and expected to work properly.

Unsupervised learning for speech motion editing

We present a new method for editing speech related facial motions. Our method uses an unsupervised learning technique, Independent Component Analysis (ICA), to extract a set of meaningful parameters without any annotation of the data. With ICA, we are able to solve a blind source separation problem and describe the original data as a linear combination of two sources. One source captures content (speech) and the other captures style (emotion). By manipulating the independent components we can edit the motions in intuitive ways.