Personalized 3-D facial expression synthesis based on landmark constraint

With the development of computer technology, 3-D facial expression synthesis has been an important and challenging task in the field of computer animation. Since the faces generated by previous works lack of personalization, we propose a novel approach for 3-D facial expression synthesis based on nonlinear learning. Firstly, a pre-process alignment is performed for input 2-D or 3-D faces with landmarks based on cylindrical mapping, and the intrinsic representations of faces are generated using radial basis function network. Secondly, according to the low dimensional representations of input faces, reconstruction operations are carried out to synthesize 3-D face expressions by sharing linear combination coefficients. Finally, the output 3-D face expressions are further optimized by its corresponding landmarks both in 2-D and 3-D spaces using locality-constrained linear coding. The experimental results indicate the robustness and effectiveness of our facial expression synthesis approach.