815666
An Efficient Markerless Method for Resynthesizing Facial Animation on an Anatomy-Based Model
This paper describes a markerless method for automatically estimating animation parameters of an anatomy-based face model from a video sequence. The method is based upon an efficient tracking procedure which employs a linear subspace model of facial appearance. During the training phase of the tracking algorithm, a set of templates is computed from the subspace basis to alleviate the online computation. The efficient tracking algorithm establishes temporal correspondence of the face region by simultaneously determining both motion and appearance parameters. Using a set of key expressions of the actor together with corresponding animation parameters of the face model, we learn a linear relationship between facial animation parameters and appearance parameters. It allows us to efficiently estimate the animation parameters from appearance parameters provided by the tracker. We present experimental results performed on synthetic and live video sequences to demonstrate the effectiveness and efficiency of our method.
59703933
Anatomy-based 3D facial modeling for expression animation
In this paper we propose a new hierarchical 3D facial model that conforms to the human facial anatomy for realistic facial expression animation. The facial model has a hierarchical biomechanical structure, incorporating a physically-based approximation to facial skin tissue, a set of anatomically-motivated facial muscle actuators and underlying skull structure. The deformable skin model has multi-layer structure to approximate different types of soft tissue. It takes into account the nonlinear stress-strain relationship of the skin and the fact that soft tissue is almost incompressible. Different kinds of muscle models have been developed to simulate the distribution of the muscle force on the skin due to muscle contraction. By the presence of the skull model, our facial model takes advantage of both more accurate facial deformation and the consideration of facial anatomy during the interactive definition of facial muscles. Under the muscular force, the deformation of the facial skin is evaluated by solving the governing dynamic equation numerically. To improve computational efficiency, we use a localized, semi-implicit integration method which allows a larger time step to be taken in the simulation while retaining stability. The dynamic facial animation algorithm runs at an interactive rate with flexible and realistic facial expressions to be generated.
45254253
A physically-based model for real-time facial expression animation
In this paper we propose a physically-based 3D dynamic facial model based on anatomical knowledge for facial expression animation. The facial model incorporates a physically-based approximation to facial skin and a set of anatomically-motivated facial muscles. The skin model is established through the use of a mass-spring system with nonlinear springs which simulate the elastic dynamics of a real facial skin. Facial muscle models are developed to emulate facial muscle contraction. Lagrangian mechanics governs the dynamics, dictating the deformation of facial surface in response to muscle forces. The dynamic facial modeling algorithm runs at interactive rate with continuous 3D display on a graphics workstation. Using our system, we can effectively synthesize realistic facial expressions and analyze tile relationship between facial surface deformation and the activation of underlying facial muscles.
31519303
Data-Driven Facial Feature Morphing for 3D Face Synthesis
43457446
Synthesis of facial expressions using a 3D anatomical model
In this paper we propose an individualized 3D facial model constructed from the anatomical perspective for realistic facial expression synthesis. Our model incorporates a physically-based approximation to facial skin tissue, a set of anatomically-motivated facial muscle actuators and underlying skull structure. The skin is modeled as a multi-layer mass-spring lattice by taking into account the nonlinear stress-strain relationship of the real skin. The 3D face model incorporates a skull structure which extends the scope of facial motion and facilitates facial muscle definition. The automatic facial muscle construction is achieved by using an efficient muscle mapping approach that ensures different muscles to be located at the anatomically correct positions between the skin and skull layers. For computational efficiency, we devise a semi-implicit integration method to numerically solve the governing dynamics. The dynamic facial animation algorithm runs at an interactive rate with realistic facial expressions to be synthesized.
39711298
Data-Driven Feature Control Models for Creating Virtual Faces
This paper presents a novel data-driven method for creating realistic face models based on estimated high-level feature control models. Our method takes as examples 3D face scans. By bringing scanned models into full correspondence with a model fitting approach, we apply principal component analysis (PCA) to the exemplar shapes of each facial feature to build a shape space. We compute a set of face anthropometric measurements to parameterize example feature shapes in the measurement spaces. Using the PCA coefficients as a compact shape representation, we approach the shape synthesis problem by forming scattered data interpolation functions that are devoted to the generation of desired shape by taking the anthropometric parameters as input. The correspondence among all exemplar textures is obtained by parameterizing a 3D generic mesh over a 2D image domain. The new feature texture with desired attributes is synthesized by interpolating the example textures.
17620967
Research on AI Planning Based Modeling Method for Area Traffic Signal Control
In this paper an area signal control model based on temporal planning is presented. Every activity of area signal control is modeled using planning domain definition language, and then the domain model of area signal control is established. Aiming at the representation problem of resource and time in the area signal control model, we extended the basic activity model by adding resource constraint and temporal constraint, and temporal planning based domain model for area signal control is established finally.
9190159
Dynamic Bayesian Network (DBN) with Structure Expectation Maximization (SEM) for Modeling of Gene Network from Time Series Gene Expression Data
Abstract Exploring gene regulatory network is a key topic in molecular biology. In this paper, we present a new dynamic Bayesian network (DBN) framework embedded with structural expectation maximization (SEM) to model gene relationship. It is well-suited for analyzing the time-series data and can deal with cyclical structures that can not be tackled by static Bayesian network. We applied the new method to learning the regulatory network and the metabolic pathway from Saccharomyces Cerevisiae cell cycle gene expression data. The results show that the proposed method is capable of handling missing values in expression data sets, and the inference accuracy can further be improved. Keyword: Microarrays; Gene regulatory networks; Dynamic Bayesian network; Structural expectation maximization 1. Introduction The establishment of gene regulatory network is critical to the understanding of the genetic regulation process. This problem has become an important challenge in recent years. The invention of microarray technology is viewed as a milestone, which helps scientist measure expression levels of thousands of genes simultaneously. Several methods have been presented so far to learn gene network from microarray data, such as Boolean networks [2, 3], differential equations [4, 5], and Bayesian networks [6-8]. Among all of them, Bayesian network based approach has received a lot of attention because of the probabilistic nature of this model. It can be used to learn causal relationship and particularly, combine it with prior knowledge readily. However, there exist a lot of shortcomings for the static version of Bayesian network. First, it is unable to capture the temporal information. Second, it is impossible to model cyclic network, which is often considered to be an accurate description of real gene regulation mechanism. In this paper, we employ dynamic Bayesian network (DBN) [1, 9,10], instead of its earlier static version, to model a gene network with cyclic regulation. In general, the DBN is well-suited for characterizing time-series gene expression data. Owing to the limitation of experimental condition, there are many missing values in the gene expression data sets, which usually have an impact on the inference accuracy. To address this problem, we propose a new DBN model embedded with
34552917
A new dynamic Bayesian network for integrating multiple data in estimating gene networks
We propose a novel method for estimating a gene regulatory network from both gene expression data and transcription factor binding location data. Based on dynamic Bayesian network (DBN) models, our method has two advantages. First, structural expectation maximization algorithm is embedded into a DBN framework, allowing it to learn the network with unknown structure and incomplete data, which cannot be tackled by expectation maximization algorithm. Second, since learning only from gene expression data is not enough to accurately estimate a gene network, we incorporate transcription factor binding location data through a structure prior. We demonstrate the effectiveness of the proposed method by the analysis of Saccharomyces Cerevisiae cell cycle data. The experimental results show that this new approach suits for dealing with missing values. Furthermore, combination of heterogeneous data from multiple sources ensures that our results are more accurate than those recovered from gene expression data alone.
15016600
Addressing Collaborative Attacks and Defense in Ad Hoc Wireless Networks
Since ad hoc networks rely on nodes cooperation to establish communication, malicious nodes can compromise the entire network. If they collaborate the devastation is even worse. Collaborative attacks may cause more devastating impacts on wireless environments than single and uncoordinated groups of attacks, as they combine efforts of more than one attacker against the target victim. In this paper, we present the most important forms of attacks,discuss possible collaborations among attackers, show how machine learning techniques and signal processing techniques can be used to detect and defend against collaborative attacks in such environments, and discuss implementation issues.
31009913
Synthesis of 3D faces using region-based morphing under intuitive control
This paper presents a new region-based method for automatically synthesizing varied, natural looking 3D human face models by morphing local facial features according to intuitive control parameters. We automatically compute a one-to-one vertex correspondence among the unregistered face scans in a large database by deforming a generic mesh to fit the specific person's face geometry in a global-to-local fashion. With the obtained correspondence, we transform the generated data sets of feature shapes into vector space representations. We parameterize the example models using the face anthropometric measurements that reflect the facial physical structure, and predefine the interpolation functions for the parameterized example models based on radial basis functions. At runtime, the interpolation functions are evaluated to efficiently generate the appropriate feature shape by taking the anthropometric parameters as input. We use a shape blending approach for generating a seamlessly deformed mesh around the feature region boundary. The correspondence among all example textures is established by parameterizing the 3D generic mesh over a 2D image domain. The new feature texture with desired attributes is synthesized by interpolating the example textures. Our 3D face synthesis method has several advantages: (1) it regulates the naturalness of synthesized faces, maintaining the quality existing in the real face examples; (2) the region-based morphing and comprehensive face shape and texture control parameters allow more diverse faces to be generated readily; and (3) the automatic runtime face synthesis is efficient in time complexity and performs fast. Copyright © 2006 John Wiley & Sons, Ltd.