Inversion Based on a Detached Dual-Channel Domain Method for StyleGAN2 Embedding

A style-based generative adversarial network (StyleGAN2) yields remarkable results in image-to-latent embedding. This work proposes a Detached Dual-channel Domain Encoder as an effective and robust method to embed an image to a latent code, i.e., GAN inversion. It infers a latent code from two aspec...

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Bibliographic Details
Published in:IEEE signal processing letters 2021, Vol.28, p.553-557
Main Authors: Yang, Nan, Zhou, MengChu, Xia, Bingjie, Guo, Xiwang, Qi, Liang
Format: Article
Language:English
Subjects:
Coders
Deep learning
Detaching
Embedding
Gallium nitride
Generative adversarial networks
Image quality
Image reconstruction
latent code optimization
Optimization
Semantics
Training
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Summary:A style-based generative adversarial network (StyleGAN2) yields remarkable results in image-to-latent embedding. This work proposes a Detached Dual-channel Domain Encoder as an effective and robust method to embed an image to a latent code, i.e., GAN inversion. It infers a latent code from two aspects: a) a detached dual-channel design to support faithful image reconstruction; and b) a local skip connection that allows conveying pieces of information with image details. We further introduce a hierarchical progressive training strategy that allows the proposed encoder to separately capture different semantic features. The qualitative and quantitative experimental results show that the well-trained encoder can embed an image into a latent code in StyleGAN2 latent space with less time than its peers while preserving facial identity and image details well.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2021.3059371