28738982
Face alive
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.
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.
2933762
Simulating human intuitive decisions by Q-learning
Simulations have become a great tool for research in the natural sciences. However their potential has not been reached far enough in the social sciences. This is in part due to the difficulty in simulating human decision making and reproducing human-like behavior. Recent advances in neo-classical decision making have defined specific differences between the decision making capabilities of rational agents and humans as well as speculations into the cause. Presented is a Q-learning model for simulating human-like decision making based upon the intuition deliberation model proposed by psychologists Kahneman and Tversky. The model is tested against the classic economic bargaining game. In this game humans and rational agents consistently converge onto distinctly different strategies. Our experiments show that a selfish agent defers from the strategy of the rational agent and is more similar to human strategy.
5191068
Predicting the activity phase of a follower neuron with A-current in an inhibitory network
The transient potassium A-current is present in most neurons and plays an important role in determining the timing of action potentials. We examine the role of the A-current in the activity phase of a follower neuron in a rhythmic feed-forward inhibitory network with a reduced three-variable model and conduct experiments to verify the usefulness of our model. Using geometric analysis of dynamical systems, we explore the factors that determine the onset of activity in a follower neuron following release from inhibition. We first analyze the behavior of the follower neuron in a single cycle and find that the phase plane structure of the model can be used to predict the potential behaviors of the follower neuron following release from inhibition. We show that, depending on the relative scales of the inactivation time constant of the A-current and the time constant of the recovery variable, the follower neuron may or may not reach its active state following inhibition. Our simple model is used to derive a recursive set of equations to predict the contribution of the A-current parameters in determining the activity phase of a follower neuron as a function of the duration and frequency of the inhibitory input it receives. These equations can be used to demonstrate the dependence of activity phase on the period and duty cycle of the periodic inhibition, as seen by comparing the predictions of the model with the activity of the pyloric constrictor (PY) neurons in the crustacean pyloric network.
7391366
Craniofacial Landmark Detection by Layered Diffusion and Dilated Skeleton Maps
This paper proposed a new method for cephalogram landmark location. It firstly employed diffusions in different scales for layered segmentation, making uses of region-homogeneity and edge-saltation, thus the landmark positions could be clearly reflected on edge-maps, i.e. skeleton, of layered diffusion. Secondly, key landmarks were determined via binarization pixel-cliques which were formed by Euclidean distance maps (EDM) on dilated skeleton. To validate the performance of this method, 30 surgery cases were inspected. The comparison of predicted parameters and application values shows that all 9 angle parameters and 4 of 5 distance parameters were correctly calculated, the predicted profiles were similar to the actual contours except one point due to the intrinsic difficulty in labium prediction. Our method took advantages of layered feature preserving and ease landmark extraction, and the latter was also owing to different EDM-Width of pixel-cliques and skeleton points. It is accurate, lifelike, and superior to many similar methods
4832713
Heteroscedastic Probabilistic Linear Discriminant Analysis with Semi-supervised Extension
Linear discriminant analysis (LDA) is a commonly used method for dimensionality reduction. Despite its successes, it has limitations under some situations, including the small sample size problem, the homoscedasticity assumption that different classes have the same Gaussian distribution, and its inability to produce probabilistic output and handle missing data. In this paper, we propose a semi-supervised and heteroscedastic extension of probabilistic LDA, called S2HPLDA, which aims at overcoming all these limitations under a common principled framework. Moreover, we apply automatic relevance determination to determine the required dimensionality of the low-dimensional space for dimensionality reduction. We empirically compare our method with several related probabilistic subspace methods on some face and object databases. Very promising results are obtained from the experiments showing the effectiveness of our proposed method.
45332140
Semantic Web Development for Traditional Chinese Medicine
The present invention relates to an improved tool grinding mechanism for grinding single point diamond cutting tools to precise roundness and radius specifications. The present invention utilizes a tool holder which is longitudinally displaced with respect to the remainder of the grinding system due to contact of the tool with the grinding surface with this displacement being monitored so that any variation in the grinding of the cutting surface such as caused by crystal orientation or tool thickness may be compensated for during the grinding operation to assure the attainment of the desired cutting tool face specifications.