Abstract: In the 3D facial expression transfer field,aiming at the two hot problems of preserving the rich detailed information of the target model to make the generated new expressions realistic and natural,and reducing the training time,this paper presents a new detailed features-preserving 3D facial expression transfer method.Firstly,the detailed features are extracted from 3D face models to obtain the basic expression models without details.Then,the basic expression of source model is transferred to the target model with the improved parametric dimensionality reduction by unsupervised regression.Finally,the detailed features of the target model are restored by using the proposed detailed feature vector adjustment strategy.The visual contrast and quantitative analysis experiments with reconstruction accuracy and training time as the evaluation indexes are conducted on the 3D facial datasets such as COMA in the Matlab software under the Windows 10 environment.The results illustrate that compared with the nonlinear co-learning method,the method can not only transfer the expression of the source model to the target model without losses,but also well preserve the personalized detailed features of the target model,so it can make the generated expressions more realistic and natural.The method also effectively improves the training speed in facial expression transfer.