Facial expression migration method and device, electronic equipment and storage medium

Abstract

The invention provides a facial expression migration method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a real facial image; inputting the real face image into a pre-trained face expression recognition network model, and outputting an action unit coefficient corresponding to a face expression in the real face image, wherein the face expressionrecognition network model is obtained through training of a virtual face data set and a real face data set, and the action unit coefficient is used for representing face data when a face is in different expressions; and driving the face of the virtual object to make a corresponding facial expression according to the action unit coefficient so as to realize facial expression migration, i.e., the facial expression recognition network model in the embodiment of the invention is obtained through joint training of the virtual facial data set and the real facial data set, so that the recognition rate is more accurate, the accuracy of facial expression recognition is improved, and the virtual object can make more realistic facial expressions.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to a method, device, electronic equipment and storage medium for transferring human facial expressions. Background technique [0002] The facial expression transfer technology has a wide range of applications in live broadcast, games, virtual people, remote conferences and other scenarios. [0003] The existing facial expression migration technology first uses the trained deep neural network model to recognize the facial expression, and then drives the virtual object to make the same expression according to the recognized facial expression. [0004] However, the data used in the training of the deep neural network model is limited, resulting in the trained deep neural network model being inaccurate, and the expressions made by virtual objects are not realistic enough and the effect is poor. Contents of the invention [0005] In view of the above problems, the present invent...

AI Technical Summary

Problems solved by technology

[0004] However, the data used in the training of the deep neural network model is limited, resulting in the training of the deep neural network model is not accurate enough, the expression made by the virtual object is not realistic enough, and the effect is poor.

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