In this paper, a new technique for modeling textured 3D faces is introduced. 3D faces can either be generated automatically from one or more photographs, or modeled directly through an intuitive user interface. Users are assisted in two key problems of computer aided face modeling. First, new face images or new 3D face models can be registered automatically by computing dense one-to-one correspondence to an internal face model. Second, the approach regulates the naturalness of modeled faces avoiding faces with an "unlikely" appearance. Starting from

Generating realistic 3D human face models and facial animations has been a persistent challenge in computer graphics. We have developed a system that constructs textured 3D face models from videos with minimal user interaction. Our system takes images and video sequences of a face with an ordinary video camera. After five manual clicks on two images to tell the system where the eye corners, nose top and mouth corners are, the system automatically generates a realistic looking 3D human head model and the constructed model can be animated immediately. A user, with a PC and an ordinary camera, can use our system to generate his/her face model in a few minutes. Keywords: Face modeling, facial animation, geometric modeling, computer vision 1 Introduction One of the most interesting and difficult problems in computer graphics is the effortless generation of realistic looking, animated human face models. Animated face models are essential to computer games, film making, online chat, ...

We have developed an easy-to-use and cost-effective system to construct textured 3D animated face models from videos with minimal user interaction. This is a particularly challenging task for faces due to a lack of prominent textures. We develop a robust system by following a model-based approach: we make full use of generic knowledge of faces in head motion determination, head tracking, model fitting, and multiple-view bundle adjustment. Our system first takes, with an ordinary video camera, images of a face of a person sitting in front of the camera turning their head from one side to the other. After five manual clicks on two images to indicate the position of the eye corners, nose tip and mouth corners, the system automatically generates a realistic looking 3D human head model that can be animated immediately (different poses, facial expressions and talking). A user, with a PC and a video camera, can use our system to generate his/her face model in a few minutes. The face model can then be imported in his/her favorite game, and the user sees themselves and their friends take part in the game they are playing. We have demonstrated the system on a laptop computer live at many events, and constructed face models for hundreds of people. It works robustly under

This thesis presents Langwidere, a facial animation system. Langwidere is intended to serve as the basis for a flexible system capable of imitating realistic characters and actions, such as speech or expressing emotion, as well as creating the exaggerated and fantastic characters found in traditional animation. In the field of computer generated characters, facial animation is difficult because of the complexity of the surface and the fact that humans have entire sections of their brains devoted to facial processing, finding and magnifying any imperfection. Typically, the construction of new characters involves tedious and repetitive labor. An unwieldy level of detail is often needed for a realistic model. Apart from any geometric consideration, animating a new character often requires reprogramming or changing and retuning a large number of arcane parameters. Numerous small, time consuming details go into creating a complete and visually interesting animated sequence. Much of this rep...

This paper presents Langwidere, a facial animation system. Langwidere is the basis for a flexible system capable of imitating a wide range of characteristics and actions, such as speech or expressing emotion. Langwidere integrates a hierarchical spline modeling system with simulated muscles based on local area surface deformation. The multi-level shape representation allows control over the extent of deformations, at the same time reducing the number of control vertices needed to define the surface. The head model is constructed from a single closed surface allowing the modeling of internal structures such as tongue and teeth, rather than just a mask. Simulated muscles are attached to various levels of the surface with more rudimentary levels substituting for bone such as the skull and jaw. The combination of a hierarchical model and simulated muscles provides precise, flexible surface control and supports easy generation of new characters with a minimum of recoding. 1. Introduction I...

This paper presents a somewhat biased survey of some well-known facial animation systems that begins with a discussion of desirable characteristics in such a system and ends with a description of one of the newest systems, currently under construction at the University of Calgary. Let us consider the characteristics of a good facial animation system. There are two separable parts, first the physical or structural model of the face, which is the shape in three dimensions. Second, the control model which can move, make small modifications to, drive, transform, and animate the physical model. Realism is one of the more difficult qualities to put into a physical model, but after you have it you can exaggerate, caricature, parody, warp, and simplify to produce anything you want. A realistic model looks like it might plausibly be a human face. To succeed with this illusion, all rendering artifacts must be avoided if possible. The requirements of flexibility, power, and ordinary common sense dictate that no code-level changes should be necessary to model a new face. When the physical model is complete, the control model animates it. The control model adds emotion, speech and facial gestures. All are necessary for a good model. If you have speech but no emotion then the face will have no character. If you have emotion but no speech then you will only be able to tell the simplest of stories. Facial gestures independent of speech and emotion are used to punctuate speech, make points, and add character. Again, no recoding should be necessary (or even desired) when another physical model is used. Motion control should be layered so that small things can be tweaked, large gestures that might be repeated can be reused on demand, and asymmetric movements effected. 2. Fascia: Frederick...

Virtual Humans are on the border of fiction and realism: while it is obvious that they do not exist in reality and function on different principles than real people, they have been endowed with human features such as being emotionally sensitive. In this article we argue that many dimensions, both human-like ones and ones made possible by the computer technology, are still unexploited to increase the effectivity and engagement of interaction with

This paper presents a novel method based on statistical facial feature control models for generating realistic controllable face models. The local feature control models are constructed based on the exemplar 3D face scans. We use a three-step model fitting approach for the 3D registration problem. Once we have a common surface representation for examples, we form feature shape spaces by applying a principal component analysis (PCA) to the data sets of facial feature shapes. We compute a set of anthropometric measurements to parameterize the exemplar shapes of each facial feature in 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. The correspondence among all exemplar face 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 exemplar textures. With the exception of an initial tuning of feature point positions and assignment of texture attribute values, our method is fully automated. In the resulting system, users are assisted in automatically generating or editing a face model by controlling the high-level parameters.

GENERATION, ESTIMATION AND TRACKING OF FACES Douglas M. DeCarlo Supervisor: Dimitris Metaxas This thesis describes techniques for the construction of face models for both computer graphics and computer vision applications. It also details model-based computer vision methods for extracting and combining data with the model. Our face models respect the measurements of populations described by face anthropometry studies. In computer graphics, the anthropometric measurements permit the automatic generation of varied geometric models of human faces. This is accomplished by producing a random set of face measurements generated according to anthropometric statistics. A face fitting these measurements is realized using variational modeling. In computer vision, anthropometric data biases face shape estimation towards more plausible individuals. Having such a detailed model encourages the use of model-based techniques---we use a physics-based deformable model framework. We derive and solve a ...

This paper presents a new hierarchical facial model that conforms to the human anatomy for realistic and fast 3D facial expression synthesis. The facial model has a skin/muscle/skull structure. The deformable skin model uses a kind of nonlinear springs to directly simulate the nonlinear visco-elastic behavior of soft tissue, and a new kind of edge repulsion springs is developed to prevent model collapse. The incorporation of the skull extends the scope of facial motion and facilitates facial muscle construction. The construction of facial muscles is achieved by using an efficient muscle mapping approach that ensures different muscles to be located at the anatomically correct positions. 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.