3D Facial Expression Synthesis Based on Nonlinear Co-learning

3D facial expression synthesis has been an important and challenging task in the field of computer animation.Inspired by the fact that facial expressions distribute on a nonlinear manifold,we propose an approach for 3D facial expression synthesis based on nonlinear co-learning.Firstly,3D facial expressions with the same attribute are projected onto an identical low dimensional representation according to the theory of nonlinear co-learning,by means of unsupervised regression.Secondly,based on the low dimensional representations of 3D faces,reconstruction operations are needed to synthesize expressions for given 3D faces,and to retarget expressions based on given expression samples.The proposed approach is able to handle noisy/incomplete input faces,and generate intact expressional faces.Experimental results show that the proposed 3D facial expression synthesis outperforms the existing methods both in quality and in efficiency.