research-article

HRInversion: High-Resolution GAN Inversion for Cross-Domain Image Synthesis

Authors:

Qi TianAuthors Info & Claims

IEEE Transactions on Circuits and Systems for Video Technology, Volume 33, Issue 5

Pages 2147 - 2161

https://doi.org/10.1109/TCSVT.2022.3222456

Published: 01 May 2023 Publication History

Abstract

We investigate GAN inversion problems of using pre-trained GANs to reconstruct real images. Recent methods for such problems typically employ a VGG perceptual loss to measure the difference between images. While the perceptual loss has achieved remarkable success in various computer vision tasks, it may cause unpleasant artifacts and is sensitive to changes in input scale. This paper delivers an important message that algorithm details are crucial for achieving satisfying performance. In particular, we propose two important but undervalued design principles: (i) not down-sampling the input of the perceptual loss to avoid high-frequency artifacts; and (ii) calculating the perceptual loss using convolutional features which are robust to scale. Integrating these designs derives the proposed framework, HRInversion, that achieves superior performance in reconstructing image details. We validate the effectiveness of HRInversion on a cross-domain image synthesis task and propose a post-processing approach named local style optimization (LSO) to synthesize clean and controllable stylized images. For the evaluation of the cross-domain images, we introduce a metric named ID retrieval which captures the similarity of face identities of stylized images to content images. We also test HRInversion on non-square images. Equipped with implicit neural representation, HRInversion applies to ultra-high resolution images with more than 10 million pixels. Furthermore, we show applications of style transfer and 3D-aware GAN inversion, paving the way for extending the application range of HRInversion.

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https://dl.acm.org/doi/10.1109/TCSVT.2023.3312790

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cover image IEEE Transactions on Circuits and Systems for Video Technology

IEEE Transactions on Circuits and Systems for Video Technology Volume 33, Issue 5

May 2023

524 pages

ISSN:1051-8215

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1051-8215 © 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 01 May 2023

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Cited By

Hu CLiu SWu X(2024)Lightweight dual-path octave generative adversarial networks for few-shot image generationMultimedia Systems10.1007/s00530-024-01484-930:5Online publication date: 20-Sep-2024
https://dl.acm.org/doi/10.1007/s00530-024-01484-9
Liu SFeng HZhou W(2023)Rethinking Supervision in Document Unwarping: A Self-Consistent Flow-Free ApproachIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.333606834:6(4817-4828)Online publication date: 23-Nov-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3336068
Li MYu WLiu QLi ZLi RZhong BZhang S(2023)Hybrid Transformers With Attention-Guided Spatial Embeddings for Makeup Transfer and RemovalIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.331279034:4(2876-2890)Online publication date: 11-Sep-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3312790

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