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Research Article | Open Access

Pyramid-VAE-GAN: Transferring hierarchical latent variables for image inpainting

College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
Advanced Technology Research Institute, Zhejiang University, Hangzhou 310027, China
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Abstract

Significant progress has been made in image inpainting methods in recent years. However, they are incapable of producing inpainting results with reasonable structures, rich detail, and sharpness at the same time. In this paper, we propose the Pyramid-VAE-GAN network for image inpainting to address this limitation. Our network is built on a variational autoencoder (VAE) backbone that encodes high-level latent variables to represent complicated high-dimensional prior distributions of images. The prior assists in reconstructing reasonable structures when inpainting. We also adopt a pyramid structure in our model to maintain rich detail in low-level latent variables. To avoid the usual incompatibility of requiring both reasonable structures and rich detail, we propose a novel cross-layer latent variable transfer module. This transfers information about long-range structures contained in high-level latent variables to low-level latent variables representing more detailed information. We further use adversarial training to select the most reasonable results and to improve the sharpness of the images. Extensive experimental results on multiple datasets demonstrate the superiority of our method. Our code is available at https://github.com/thy960112/Pyramid-VAE-GAN.

Keywords

image inpaintingvariational autoencoder (VAE)latent variable transfer (LTN);pyramid structuregenerative model

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Computational Visual Media
Volume 9 Issue 4,
December 2023
Pages 827-841
DOI: 10.1007/s41095-022-0331-3
Cite this article:
Tian H, Zhang L, Li S, et al. Pyramid-VAE-GAN: Transferring hierarchical latent variables for image inpainting. Computational Visual Media, 2023, 9(4): 827-841. https://doi.org/10.1007/s41095-022-0331-3

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Received: 02 May 2022
Accepted: 18 December 2022
Published: 27 July 2023
© The Author(s) 2023.

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