A Diversified Face Image Synthesis Method and System

Abstract

The invention provides a variety of human face image synthesis methods and systems. Wherein the method includes obtaining the source face picture, the target face picture and attribute label information; according to the source face picture, the target face picture and the face synthesis network model, the source face expression, the target face identity feature and the specified Realistic face pictures of attributes; the face synthesis network model includes a face feature point generator and a geometry-attribute-aware generator; the face feature point generator is used to extract the feature points of the source face and the target face as geometric features of the face information, and extract the expression information from the geometric feature information of the face, and transfer the expression information of any source face to the target face in the latent space; the geometry-attribute-aware generator is used to extract the identity from the target face and the label respectively Feature and specified attribute information, combined with expression information, generates a realistic face picture with source facial expression, target face identity characteristics and specified attributes.

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

[0005] The inventors found that the current face image synthesis based on deep learning still has the following problems: the controllability and diversity of face image synthesis are poor, and it is difficult to obtain faces with various appearances and rich expressions that meet user expectations ; The synthesized face cannot well maintain the given identity features, and the expression is unreal and natural; the synthesis efficiency and generalization ability of the face image are low

Images