A probabilistic approach to realistic face synthesis
Hyunjung Shim, Inwoo Ha, Taehyun James Rhee, James Dokyoon Kim, Chang-Yeong Kim · 2010
This paper presents a novel approach to face modeling for realistic synthesis, powered by a probabilistic face diffuse model and a generic face specular map. We first construct a probabilistic face diffuse model for estimating the albedo and the normals of a face from an unknown input image. Then, we introduce a generic face specular map for estimating the specularity of the face. Using the estimated albedo, normal and specular information, we can synthesize the face under arbitrary lighting and viewing directions realistically. Unlike many existing face modeling techniques, our approach can retain both the diffuse and specular properties of the face without involving an elaborating 3D matching procedure. Thanks to the compact representation and the effective inference scheme, our technique can be applied to many practical applications, such as face normalization, avatar creation and de-identification.