Unlike the human face, the neck has been largely overlooked in the computer graphics literature, this despite its complex anatomical structure and the important role that it plays in supporting the head in balance while generating the controlled head movements that are essential to so many aspects of human behavior. This paper makes two major contributions. First, we introduce a biomechanical model of the human head-neck system. Emulating the relevant anatomy, our model is characterized by appropriate kinematic redundancy (7 cervical vertebrae coupled by 3-DOF joints) and muscle actuator redundancy (72 neck muscles arranged in 3 muscle layers). This anatomically consistent biomechanical model confronts us with a challenging motor control problem, even for the relatively simple task of balancing the mass of the head in gravity atop the cervical spine. Hence, our second contribution is a novel neuromuscular control model for human head animation that emulates the relevant biological motor control mechanisms. Incorporating low-level reflex and high-level voluntary sub-controllers, our hierarchical controller provides input motor signals to the numerous muscle actuators. In addition to head pose and movement, it controls the tone of mutually opposed neck muscles to regulate the stiffness of the headneck multibody system. Employing machine learning techniques, the neural networks within our neuromuscular controller are trained offline to efficiently generate the online pose and tone control signals necessary to synthesize a variety of autonomous movements for the behavioral animation of the human head and face. Copyright © 2006 by the Association for Computing Machinery, Inc.
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