Issue No. 09 - Sept. (2012 vol. 18)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2011.157
A. Dhall , RSISE, Australian Nat. Univ., Canberra, ACT, Australia
M. de la Hunty , Australian Nat. Univ., Canberra, ACT, Australia
A. Asthana , RSISE, Australian Nat. Univ., Canberra, ACT, Australia
R. Goecke , Fac. of Inf. Sci. & Eng., Univ. of Canberra, Canberra, ACT, Australia
The issue of transferring facial performance from one person's face to another's has been an area of interest for the movie industry and the computer graphics community for quite some time. In recent years, deformable face models, such as the Active Appearance Model (AAM), have made it possible to track and synthesize faces in real time. Not surprisingly, deformable face model-based approaches for facial performance transfer have gained tremendous interest in the computer vision and graphics community. In this paper, we focus on the problem of real-time facial performance transfer using the AAM framework. We propose a novel approach of learning the mapping between the parameters of two completely independent AAMs, using them to facilitate the facial performance transfer in a more realistic manner than previous approaches. The main advantage of modeling this parametric correspondence is that it allows a "meaningful” transfer of both the nonrigid shape and texture across faces irrespective of the speakers' gender, shape, and size of the faces, and illumination conditions. We explore linear and nonlinear methods for modeling the parametric correspondence between the AAMs and show that the sparse linear regression method performs the best. Moreover, we show the utility of the proposed framework for a cross-language facial performance transfer that is an area of interest for the movie dubbing industry.
solid modelling, cinematography, image texture, regression analysis, movie dubbing industry, parametric correspondence, computer graphics community, active appearance model, deformable face model-based approaches, computer vision, nonrigid shape, nonrigid texture, sparse linear regression method, cross-language facial performance transfer, Face, Shape, Computational modeling, Active appearance model, Training, Deformable models, Solid modeling, face modeling and animation., Active appearance models, facial performance transfer
A. Dhall, M. de la Hunty, A. Asthana and R. Goecke, "Facial Performance Transfer via Deformable Models and Parametric Correspondence," in IEEE Transactions on Visualization & Computer Graphics, vol. 18, no. , pp. 1511-1519, 2012.