Face Image Inpainting via Variational Autoencoder

被引：0

作者：

Zhang X. ^{[1
,2
]}

Cheng L. ^{[2
]}

Bai S. ^{[2
]}

Zhang F. ^{[3
]}

Sun N. ^{[1
]}

Wang Z. ^{[2
]}

机构：

[1] College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao

[2] State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou

[3] College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou

来源：

Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2020年 / 32卷 / 03期

关键词：

Discriminative network; Disentanglement; Image fusion; Image inpainting; Variational autoencoder;

D O I：

10.3724/SP.J.1089.2020.17938

中图分类号：

学科分类号：

摘要：

Face image inpainting based on the convolutional neural network has enabled a variety of applications, ranging from criminal investigation to cultural relics protection. However, the results of existing methods are often limited to insufficient diversity and still far from realistic. In this work, we generate more reasonable missing facial content with a variant of variational auto-encoder, as well as generative adversarial network. Furthermore, we impose constraints on latent variables to encourage the distribution of representations to be factorial that making them independent across dimensions. Latter, the optimal boundary was obtained through dynamic programming, and finally we get the seamless results by Poisson image editing. Experiments on CelebA dataset demonstrated that the proposed method achieved better inpainting results and disentanglement. © 2020, Beijing China Science Journal Publishing Co. Ltd. All right reserved.

引用

页码：401 / 409

页数：8

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