High-Fidelity GAN Inversion for Image Attribute Editing
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2021
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| _version_ | 1866909327336407040 |
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| author | Wang, Tengfei Zhang, Yong Fan, Yanbo Wang, Jue Chen, Qifeng |
| author_facet | Wang, Tengfei Zhang, Yong Fan, Yanbo Wang, Jue Chen, Qifeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2109_06590 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | High-Fidelity GAN Inversion for Image Attribute Editing Wang, Tengfei Zhang, Yong Fan, Yanbo Wang, Jue Chen, Qifeng Computer Vision and Pattern Recognition 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. |
| title | High-Fidelity GAN Inversion for Image Attribute Editing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2109.06590 |