This paper presents a novel method for generating varied, realistic geometric face models by synthesizing facial features according to anthropometric parameters. Our method takes as examples scanned 3D face models in order to exploit the parameter-to-geometry correlations that are presented in the real faces. A two-step model fitting approach is used to establish correspondences among scanned models. We transform the data sets of facial feature shape into vector space representations by applying a principal component analysis (PCA). For each facial feature, we compute a set of anthropometric measurements to parameterize the exemplar meshes into a measurement space. Using 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.

In document digitization through camera-based systems, simple imaging setups often produce geometric distortions in the resultant 2D images because of the non-planar geometric shapes of certain documents such as thick bound books, rolled, folded or crumpled materials, etc. Previous works have demonstrated that arbitrary warped documents can be successfully restored by flattening a 3D scan of the document. These approaches use physically-based or relaxation-based techniques in their flattening process. While this has been demonstrated to be effective in rectifying the image content and improving OCR, these previous approaches have several limitations in terms of speed and stability. In this paper, we propose a distance-based penalty metric to replace the mass-spring model and introduce additional bending resistance and drag forces to improve the efficiency of the existing approaches. The use of Verlet integration and special plane collision handling schemes also help to achieve better stability without sacrificing efficiency. Experiments on various document images captured from books, brochures and historical documents with arbitrary warpings have demonstrated large improvements over the existing approaches in terms of stability and efficiency.

This paper presents a geometric wrinkle model based on facial muscles for realistically and efficiently simulating wrinkles generated in facial expressions. Our method simulates wrinkles on an anatomy-based face model that mimics the layered structure of skin, muscles, and skull for facial animation. Corresponding to three types of facial muscles, a geometric model is developed to govern how the wrinkle amplitude evolves locally upon skin deformation. By taking into account the properties of real wrinkles, it provides intuitive parameters for easy control over wrinkle characteristics. During facial animation, wrinkles are generated in the local regions influenced by muscle contraction, simulating resistance to compression of tissues. In the simulation, an adaptive refinement automatically adapts the local resolution at which potential inaccuracies arc detected depending on local deformation. Thanks to its geometric nature, our method simulates wrinkles that can be dynamically rendered at interactive rates.

We present a framework for acquiring and restoring images of warped documents to their original planar shapes. Image capturing of warped documents often results in warped images. Most digital restoration approaches make use of 2D image processing methods which are dependent on the content of the images. Some recent work attempts to remove this constraint by working directly on the 3D shape of the warped documents. In our framework, we improve on this recent approach to make it more efficient and stable. First we capture the warped surface representation through a set of active lighting from a laser range scanner onto the document surface. This gives us an accurate 3D representation of the document shape. Next by using a stick constraint and a stable numerical integrator, we can digitally flatten the 3D model to a planar shape. This simple approach avoids potential instability problem and is fast. Restoration results for a few types of warped documents are demonstrated and a significant improvement over the original images can be observed.

In this paper a new adaptation-based method is presented to reconstruct animatable facial models of human individuals from scan data. An anatomy-based generic control model serves as the starting point for our adaptation algorithm. Based on a series of measurements between the specified 3D landmarks, a global adaptation is carried out to align the generic control model to the scan surface. A local adaptation then deforms the geometry of the generic model to fit all of its vertices to the scan surface. The high-resolution geometry of the scan surface is represented as a displacement map which is generated using an offset-envelope mapping. Reconstruction of high-resolution geometry on the adapted generic mesh is achieved by hierarchical refinement using a surface subdivision scheme with resampling of the displacement map

Advances in 3D scanning technology have enabled automatic capture of complex 3D models such as human faces with highly detailed surfaces. However, the range data cannot be used easily for animatable face modeling due to the absence of functional animation structure and dense surface data. The paper presents an automatic facial model adaptation algorithm for reconstruction of animatable individualized 3D facial models from range data. A generic model that represents both the face shape and anatomical structure serves as the starting point for the adaptation algorithm. The global adaptation transforms the generic model to align it with the scan data in the 3D space based on measurements between a set of 3D landmarks. The local adaptation then deforms the skin mesh of the generic model to fit all of its vertices to the scan surface. The underlying muscle structure is automatically adapted and facial texture is transferred. The reconstructed 3D face resembles the shape and color of a real individual and can be animated immediately with muscle parameters.

This paper presents a new physically-based 3D facial model based on anatomical knowledge which provides high fidelity for facial expression animation while optimizing the computation. Our facial model has a multilayer biomechanical structure, incorporating a physically-based approximation to facial skin tissue, a set of anatomically-motivated facial muscle actuators, and underlying skull structure. In contrast to existing mass-spring-damper (MSD) facial models, our dynamic skin model uses the nonlinear springs to directly simulate the nonlinear visco-elastic behavior of soft tissue and a new kind of edge repulsion spring is developed to prevent collapse of the skin model. Different types of muscle models have been developed to simulate distribution of the muscle force applied on the skin due to muscle contraction. The presence of the skull advantageously constrain the skin movements, resulting in more accurate facial deformation and also guides the interactive placement of facial muscles. The governing dynamics are computed using a local semi-implicit ODE solver. In the dynamic simulation, an adaptive refinement automatically adapts the local resolution at which potential inaccuracies are detected depending on local deformation. The method, in effect, ensures the required speedup by concentrating computational time only where needed while ensuring realistic behavior within a predefined error threshold. This mechanism allows more pleasing animation results to be produced at a reduced computational cost.

Abstract This paper presents new methods for efficient modeling and animation of an hierarchical facial model that conforms to the human face anatomy for realistic and fast 3D facial expression synthesis. The facial model has a skin–muscle–skull structure. The deformable skin model directly simulates the nonlinear visco‐elastic behavior of soft tissue and effectively prevents model collapse. The construction of facial muscles is achieved by using an efficient muscle mapping approach. Based on a cylindrical projection of the texture‐mapped facial surface and wire‐frame skin and skull meshes, this approach ensures different muscles to be located at the anatomically correct positions between the skin and skull layers. For computational efficiency, we devise an adaptive simulation algorithm which uses either a semi‐implicit integration scheme or a quasi‐static solver to compute the relaxation by traversing the designed data structures in a breadth‐first order. The algorithm runs in real‐time and has successfully synthesized realistic facial expressions. ACM CSS:I.3.5 Computer Graphics: Computational Geometry and Object Modelling—physically based modelling;I.3.7 Computer Graphics: Three‐Dimensional Graphics and Realism—animation;

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.

We present a system for creating a photorealistic 3D face model of a specific person with a hierarchical structure for dynamic facial expression animation. The facial modeling procedure starts from the individual facial measurements which provides a highly accurate facial geometry and color information. Based on the reconstructed facial mesh, a deformable multi-layer soft tissue model is developed to simulate the dynamic behavior of the skin by taking into account its nonlinear stress-strain relationship. The underlying muscle and skull structure are physically modeled and integrated with the skin meshes. By taking as input a collection of reflectance images covering the face, the view-based texture blending method automatically generates a comprehensive texture map for photorealistic rendering. The resulting personalized face model with biomechanical structure is animated by numerically solving the governing dynamic equation. Using our system, we have synthesized realistic expressions on the authentic face model of a specific individual.

The paper presents a physically-based 3D 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. Muscle models are developed to emulate facial muscle contraction. Lagrangian mechanics governs the dynamics, dictating the deformation of facial surface in response to muscular forces. We show that when surface regions are influenced by the large muscular force, the local deformation becomes inaccurate. The conventional method to deal with this problem is using a fine network, but it also increases the cost of computation. We therefore present an approach to adaptively refine the mass-spring facial model to a required accuracy. It generates more pleasing results at low computational expense.

We propose a physically-based approach based on anatomical knowledge for real-time facial expression animation. The facial model incorporates a physically-based approximation to facial skin and a set of anatomically-motivated facial muscles. The skin is modeled by a mass-spring system with nonlinear springs which have a biphasic stress-strain relationship to simulate the elastic dynamics of real facial skin. Facial muscles are modeled as forces deforming the spring mesh. Based on the action units (AUs) of the facial action coding system (FACS), various expressions can be generated by a combination of contractions of a set of facial muscles. Lagrangian mechanics governs the dynamics, dictating the deformation of the facial surface in response to muscle forces. Simulation results show real-time facial deformation as well as realistic expression animation.

This paper presents a space-time adaptive refinement approach to simulate facial expressions on the deformable 3D face model at interactive rate. This new computational model benefits from an adaptive sampling of both space and time to minimize computational cost. Using this technique, we achieve a guaranteed frame rate of the dynamic facial expression simulation at low computational expense.