17912176
Anatomy-based face reconstruction for animation using multi-layer deformation
This paper presents a novel multi-layer deformation (MLD) method for reconstructing animatable, anatomy-based human facial models with minimal manual intervention. Our method is based on adapting a prototype model with the multi-layer anatomical structure to the acquired range data in an ''outside-in'' manner: deformation applied to the external skin layer is propagated along with the subsequent transformations to the muscles, with the final effect of warping the underlying skull. The prototype model has a known topology and incorporates a multi-layer structure hierarchy of physically based skin, muscles, and skull. In the MLD, a global alignment is first carried out to adapt the position, size, and orientation of the prototype model to align it with the scanned data based on measurements between a subset of specified anthropometric landmarks. In the skin layer adaptation, the generic skin mesh is represented as a dynamic deformable model which is subjected to internal force stemming from the elastic properties of the surface and external forces generated by input data points and features. A fully automated approach has been developed for adapting the underlying muscle layer which consists of three types of physically based facial muscle models. MLD deforms a set of automatically generated skull feature points according to the adapted external skin and muscle layers. The new positions of these feature points are then used to drive a volume morphing applied to the template skull model. We demonstrate our method by applying it to generate a wide range of different facial models on which various facial expressions are animated.
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.
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.
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.
31519303
Data-Driven Facial Feature Morphing for 3D Face Synthesis
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.
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.
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.
18773440
An Agent-based Simulation Study for Exploring Organizational Adaptation
Agile enterprises with different organizational structures manifest different organizational behaviors when responding to environmental changes. In this paper, we use a computational model to examine organizational adaptation on four dimensions: Agility, Robustness, Resilience, and Survivability. We analyze the dynamics of organizational adaptation by a simulation study on the interaction between tasks and organization in a sales enterprise. The `what if' analyses in different scenarios show that more flexible communication between employees and less hierarchy level with the suitable centralization can improve organizational adaptation. The developed simulation model supports the exploration of the parametric space that defines alternative organizing processes for the optimal strategy given the specified environmental dynamics.
2698498
An Approach for Adaptive DRAM Temperature and Power Management
High-performance DRAMs are providing increasing memory access bandwidth to processors, which is leading to high power consumption and operating temperature in DRAM chips. In this paper, we propose a customized low-power technique for high-performance DRAM systems to improve DRAM page hit rate by buffering write operations that may incur page misses. This approach reduces DRAM system power consumption and temperature without any performance penalty. We combine the throughput-aware page-hit-aware write buffer (TAP) with low-power-state-based techniques for further power and temperature reduction, namely, TAP-low. Our experiments show that a system with TAP-low could reduce the total DRAM power consumption by up to 68.6% (19.9% on average). The steady-state temperature can be reduced by as much as 7.84°C and 2.55°C on average across eight representative workloads.
7672026
A power and temperature aware DRAM architecture
Technological advances enable modern processors to utilize increasingly larger DRAMs with rising access frequencies. This is leading to high power consumption and operating temperature in DRAM chips. As a result, temperature management has become a real and pressing issue in high performance DRAM systems. Traditional low power techniques are not suitable for high performance DRAM systems with high bandwidth. In this paper, we propose and evaluate a customized DRAM low power technique based on page hit aware write buffer (PHA-WB). Our proposed approach reduces DRAM system power consumption and temperature without any performance penalty. Our experiments show that a system with a 64-entry PHA-WB could reduce the total DRAM power consumption by up to 22.0% (9.6% on average). The peak and average temperature reductions are 6.1degC and 2.1degC, respectively.