Computer Science ›› 2023, Vol. 50 ›› Issue (2): 221-230.doi: 10.11896/jsjkx.220800166

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Lightweight Face Generation Method Based on TransEditor and Its Application Specification

LIANG Weiliang1, LI Yue2, WANG Pengfei3   

  1. 1 Blockchain Research Institute,Renmin University of China,Beijing 100872,China
    2 School of Economics,Shenzhen Polytechnics,Shenzhen,Guangdong 518055,China
    3 Big Data Center,Ministry of Emergency Management of the people's Republic of China,Beijing 100013,China
  • Received:2022-08-16 Revised:2022-10-01 Online:2023-02-15 Published:2023-02-22
  • Supported by:
    Youth Foundation of Social Science and Humanity,China Ministry of Education(21YJC820023) and China Postdoctoral Science Foundation(2022M713439)

Abstract: Face generation can combine the style of the face and the pose of the head to synthesize fake face images,it is often used for vision tasks such as gender conversion and pose modification.GAN-based face generation methods can greatly improve the quality and editability of face generation.However,these generation methods have complex network structures and large computing resource requirements,and are difficult to directly apply to practical scenarios.To achieve efficient face generation,this paper proposes a lightweight face generation method based on TransEditor,and discusses the corresponding application specifications.At the technical level,firstly,based on the TransEditor face editing network model,we design a lightweight face generation network model with reference to the generator structure of lightweight network model such as StyleGAN2.Secondly,we analyze the loss function of the network model from the aspects of generation loss,confrontation loss,reconstruction loss,etc.,and propose to use the PReLU activation function instead of the Softplus activation function to improve the generation effect of the ge-nerator.Finally,through massive experiments,it is proved that the LPIPS of the proposed lightweight face generation method based on TransEditor only reduces by 0.0042,which greatly reduces the training time and parameter amount of the model,and improves the operation efficiency of the face generation model.At the level of application specifications,it is necessary to improve the existing regulatory measures and standardize the use of the proposed face generation method,so that technological progress can better serve social development.

Key words: Face generation, Generative adversarial network, Transformer network, Lightweight, Application specification

CLC Number: 

  • TP301.6
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