Efficient facial animation integrating Euclidean and geodesic distance-based algorithms into radial basis function interpolation.

Abstract

Facial animation has been an important research topic during the last decades. There has been considerable effort on the efficient creation of realistic and believable facial expressions. Among various approaches for creating believable movements in human facial features, one of the most common utilises motion capture. This thesis explores the current approaches on facial animation with this technology together with Radial Basis Function (RBF) interpolation, covering a review of Euclidean and geodesic distance-based algorithms, and proposing a hybrid approach that tries to take the advantages of the two previous methods aided by pre-processed distance data to fasten the computations. Using motion capture performance based on the Facial Action Coding System (FACS), the results are then evaluated with a wide range of facial expressions in both a realistic and a stylised facial model. The findings of this thesis show the advantage of the hybrid RBF approach proposed which, combined with pre-processed distance data, results in a more efficient and more accurate process for the generation of high-detail facial animation with motion capture.