Investigating facial animation production through artistic inquiry

Studies into dynamic facial expressions tend to make use of experimental methods based on objectively manipulated stimuli. New techniques for displaying increasingly realistic facial movement and methods of measuring observer responses are typical of computer animation and psychology facial expression research. However, few projects focus on the artistic nature of performance production. Instead, most concentrate on the naturalistic appearance of posed or acted expressions. In this paper, the authors discuss a method for exploring the creative process of emotional facial expression animation, and ask whether anything can be learned about authentic dynamic expressions through artistic inquiry

Improvements on a simple muscle-based 3D face for realistic facial expressions

Facial expressions play an important role in face-to-face communication. With the development of personal computers capable of rendering high quality graphics, computer facial animation has produced more and more realistic facial expressions to enrich human-computer communication. In this paper, we present a simple muscle-based 3D face model that can produce realistic facial expressions in real time. We extend Waters' (1987) muscle model to generate bulges and wrinkles and to improve the combination of multiple muscle actions. In addition, we present techniques to reduce the computation burden on the muscle mode

3d Computer Modeling Of Human Mandible Motion With Application To Human Facial Motion

Computer facial modeling and animation has been an interest of computer graphics researchers for many years. This is not only because the face itself is an interesting object, but also because facial animation finds application in many other disciplines (for example, entertainment, medical education, telecommunication, psychology, medicine, and linguistics). Because the mandible motion plays a major role in modeling facial motion, its study is of significance to each of those disciplines as well. In addition, the mandible itself is an object of study in the area of clinical science.;Current facial movement models in computer animation have difficulty dealing with facial movements that are strongly determined by the mandible, such as chewing. This thesis proposes new computer models of the mandible that address this problem. First, a geometric mandible model is proposed to simulate typical motion features of the mandible such as opening, closing, protruding, and lateral shifting. While this model is simple and successful, it has drawbacks when applied to motions as complicated as chewing. Therefore, a physically-based model is also proposed to deal with these drawbacks. This physically-based mandible model is then integrated with a spring-based physical facial model to automatically simulate motions such as chewing

Computer facial animation: a review

Computer facial animation is not a new endeavour as it had been introduced since 1970s. However, animating human face still presents interesting challenges because of its familiarity as the face is the part used to recognize individuals.Facial modelling and facial animation are important in developing realistic computer facial animation. Both modelling and animation is dependent to drive the animation.This paper reviews several geometric -based modelling (shape interpolation,parameterization and muscle-based animation)and data-driven animation (image-based techniques speech-driven techniques and performance-driven animation) techniques used in computer graphics and vision for facial animation. The main concept s and problems for each technique are highlighted in the paper

A Survey of Computer Graphics Facial Animation Methods: Comparing Traditional Approaches to Machine Learning Methods

Human communications rely on facial expression to denote mood, sentiment, and intent. Realistic facial animation of computer graphic models of human faces can be difficult to achieve as a result of the many details that must be approximated in generating believable facial expressions. Many theoretical approaches have been researched and implemented to create more and more accurate animations that can effectively portray human emotions. Even though many of these approaches are able to generate realistic looking expressions, they typically require a lot of artistic intervention to achieve a believable result. To reduce the intervention needed to create realistic facial animation, new approaches that utilize machine learning are being researched to reduce the amount of effort needed to generate believable facial animations. This survey paper summarizes over 20 research papers related to facial animation and compares the traditional animation approaches to newer machine learning methods as well as highlights the strengths, weaknesses, and use cases of each different approach

Facial Expression Recognition Using SVM Classifier

Facial feature tracking and facial actions recognition from image sequence attracted great attention in computer vision field. Computational facial expression analysis is a challenging research topic in computer vision. It is required by many applications such as human-computer interaction, computer graphic animation and automatic facial expression recognition. In recent years, plenty of computer vision techniques have been developed to track or recognize the facial activities in three levels. First, in the bottom level, facial feature tracking, which usually detects and tracks prominent landmarks surrounding facial components (i.e., mouth, eyebrow, etc), captures the detailed face shape information; Second, facial actions recognition, i.e., recognize facial action units (AUs) defined in FACS, try to recognize some meaningful facial activities (i.e., lid tightener, eyebrow raiser, etc); In the top level, facial  expression analysis attempts to recognize some meaningful facial activities (i.e., lid tightener, eyebrow raiser, etc); In the top level, facial expression analysis attempts to recognize facial expressions that represent the human emotion states. In this proposed algorithm initially detecting eye and mouth, features of eye and mouth are extracted using Gabor filter, (Local Binary Pattern) LBP and PCA is used to reduce the dimensions of the features. Finally SVM is used to classification of expression and facial action units

Final Report to NSF of the Standards for Facial Animation Workshop

The human face is an important and complex communication channel. It is a very familiar and sensitive object of human perception. The facial animation field has increased greatly in the past few years as fast computer graphics workstations have made the modeling and real-time animation of hundreds of thousands of polygons affordable and almost commonplace. Many applications have been developed such as teleconferencing, surgery, information assistance systems, games, and entertainment. To solve these different problems, different approaches for both animation control and modeling have been developed