The face is the center of an individual's sense of identity and self-esteem, and plays a crucial role in interpersonal relationships. The current state of the art approaches computational modeling of human faces from two distinct angles. Computer graphics models feature high visual realism, as seen in the movies. Whereas biomechanics focuses on physical realism, modeling the face as a sophisticated mechanical system that obeys the laws of physics. This project will bridge the gap between these two viewpoints and construct models of the face that offer both visual and physical realism. This is of the utmost importance in applications such as surgical prediction. Close to five percent of the population of the United States has a dentofacial anomaly that may require jaw surgery, which can have a profound effect on the appearance of the face. Virtually every patient asks, "How will I look after the treatment?" Even though this is an important and well-studied problem, there are currently no methods capable of predicting post-operative changes in facial expressions. By combining both visual and physical realism, this research will create the first system that can provide a natural, 3D visual answer to the patient's question by displaying a photorealistic facial animation after a simulated surgical procedure. Additional broad impact will derive from project outcomes because the new numerical techniques for the efficient simulation of biomaterials will provide a reusable foundation that can be leveraged for computational modeling of a variety of engineering materials that exhibit pronounced heterogeneity and anisotropy. The anatomical modeling framework developed in this work will also serve as a launchpad for future inquiry of interest to medical science (modeling of soft-tissue surgery, exploration of aging or pathology in the mechanics of facial expression, etc.).
To these ends, the project will create algorithms for the automated development of accurate patient-specific models of facial anatomy capable of representing realistic behavior of soft tissues, including the formation of facial expressions. The research aims at challenges which require coordinated efforts across various disciplines, including computer graphics, computer vision, biomechanics and craniofacial surgery. Novel computer vision methods will leverage information from 3D imaging (MRI/CT) to capture details of in-vivo human face deformations. The acquired data will serve as input to inverse finite element solvers, which will compute the unknown mechanical parameters of person-specific soft tissues, accounting for pre-strain and muscle activation units. This data-centric approach is a departure from established model-building methodologies, and has the potential to make a transformative impact on the anatomical modeling field. Furthermore, although the clinical application of orthognathic surgery is used as the motivation and key benchmark for the work, the algorithmic innovations produced in this activity transcend the specific scope of this task and deliver broader utility in the fields of visual computing and computational dynamics. Physics-based models of shape and deformation of elastic objects will be incorporated into visual acquisition systems as structural priors, enhancing the robustness and accuracy of the data collection.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
