Diversified face image synthesis method and system

Abstract

The invention provides a diversified face image synthesis method and system. The method comprises the following steps: acquiring a source face picture, a target face picture and attribute tag information; according to the source face picture, the target face picture and the face synthesis network model, obtaining a realistic face picture with a source face expression, a target face identity feature and a specified attribute, wherein the face synthesis network model comprises a face feature point generator and a geometry-attribute perception generator; the face feature point generator is used for extracting feature points of a source face and a target face as face geometric feature information, extracting expression information from the face geometric feature information, and migrating the expression information of any source face to the target face in a potential space; and the geometry-attribute perception generator is used for correspondingly extracting identity features and specified attribute information from the target face and the label respectively, and generating a realistic face picture with a source face expression, the target face identity features and specified attributes in combination with the expression information.

Description

technical field [0001] The invention belongs to the field of human face image synthesis, and in particular relates to a diversified human face image synthesis method and system. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Face image synthesis is a research hotspot and difficult problem in the field of computer vision and computer graphics. It has a wide range of applications in the fields of digital entertainment, public security, and medical and health. The goal of face image synthesis is to generate expected images based on input information. High-quality face pictures of expression and appearance (including facial features, hair color, age, gender, etc.). [0004] With the rise and development of deep learning technology, data-driven face image synthesis technology has made great breakthroughs. The research of Susskind et al. is o...

AI Technical Summary

Problems solved by technology

In order to solve the problem that the paired training data required by the method is difficult to obtain, CycleGAN introduces a cycle consistency adversarial loss function in the generative adversarial network framework, which does not require paired facial expression pictures for training, but the problem is that the model After the training is completed, it can only be converted between two specific characters, which limits the efficiency and scalability of the model

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