Summary of Gradual Residuals Alignment: a Dual-stream Framework For Gan Inversion and Image Attribute Editing, by Hao Li et al.
Gradual Residuals Alignment: A Dual-Stream Framework for GAN Inversion and Image Attribute Editing
by Hao Li, Mengqi Huang, Lei Zhang, Bo Hu, Yi Liu, Zhendong Mao
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a novel dual-stream framework for GAN-based image attribute editing, which leverages GAN Inversion to project real images into the latent space of GAN. The approach manipulates corresponding latent codes to edit attributes while preserving details. Recent inversion methods have mainly relied on additional high-bit features to improve image details preservation, but existing works fail to accurately complement lost details during editing, leading to inconsistent content and artifacts in edited images. This work argues that details should be gradually injected into both the reconstruction and editing process in a multi-stage coarse-to-fine manner for better detail preservation and high editability. The proposed framework consists of a Reconstruction Stream to embed lost details into residual features and an Editing Stream using Selective Attention mechanism to accurately align and inject these details into the editing process. Experimental results demonstrate the superiority of this framework in both reconstruction accuracy and editing quality compared with existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to edit images using GANs. It uses a special technique called GAN Inversion to turn real images into code that can be edited. The problem is that current methods lose details when editing, making the results look unnatural. To solve this, the researchers propose a two-part process: one part adds lost details back in and another part carefully injects these details into the edited image. This approach improves both how well the original image is reconstructed and the quality of the edited result. |
Keywords
» Artificial intelligence » Attention » Gan » Latent space
