High-Fidelity GAN Inversion for Image Attribute Editing
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Cited by 32 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
We present a novel high-fidelity generative adversarial network (GAN) inversion framework that enables attribute editing with image-specific details well-preserved (e.g., background, appearance, and illumination). We first analyze the challenges of high-fidelity GAN inversion from the perspective of lossy data compression. With a low bit-rate latent code, previous works have difficulties in preserving high-fidelity details in reconstructed and edited images. Increasing the size of a latent code can improve the accuracy of GAN inversion but at the cost of inferior editability. To improve image fidelity without compromising editability, we propose a distortion consultation approach that employs a distortion map as a reference for high-fidelity reconstruction. In the distortion consultation inversion (DCI), the distortion map is first projected to a high-rate latent map, which then complements the basic low-rate latent code with more details via consultation fusion. To achieve high-fidelity editing, we propose an adaptive distortion alignment (ADA) module with a self-supervised training scheme, which bridges the gap between the edited and inversion images. Extensive experiments in the face and car domains show a clear improvement in both inversion and editing quality.
Key facts. Cited by 197 papers in the map (data through 7 Oct 2026). Builds on 43 of its 51 references that are in the map. First posted on arXiv 14 Sep 2021.
Builds on
43 of 51 references.
References · 43
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31 more
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- 2021Designing an Encoder for StyleGAN Image Manipulationcited by 542
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- 2021Pivotal Tuning for Latent-based Editing of Real Imagescited by 427
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- 2019Image2StyleGAN++: How to Edit the Embedded Images?cited by 356
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- 2020Towards Fast, Accurate and Stable 3D Dense Face Alignmentcited by 219
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- 2021LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directionscited by 52
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Citing papers per year: 2021: 6; 2022: 49; 2023: 74; 2024: 40; 2025: 20; 2026: 8.
197 papers.
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