StyleRes: Transforming the Residuals for Real Image Editing with StyleGAN

• URL: http://arxiv.org/abs/2212.14359v1
• Date: Thu, 29 Dec 2022 16:14:09 GMT
• Title: StyleRes: Transforming the Residuals for Real Image Editing with StyleGAN
• Authors: Hamza Pehlivan, Yusuf Dalva, Aysegul Dundar
• Abstract summary: Inverting real images into StyleGAN's latent space is an extensively studied problem. Trade-off between the image reconstruction fidelity and image editing quality remains an open challenge. We present a novel image inversion framework and a training pipeline to achieve high-fidelity image inversion with high-quality editing.
• Score: 4.7590051176368915
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a novel image inversion framework and a training pipeline to achieve high-fidelity image inversion with high-quality attribute editing. Inverting real images into StyleGAN's latent space is an extensively studied problem, yet the trade-off between the image reconstruction fidelity and image editing quality remains an open challenge. The low-rate latent spaces are limited in their expressiveness power for high-fidelity reconstruction. On the other hand, high-rate latent spaces result in degradation in editing quality. In this work, to achieve high-fidelity inversion, we learn residual features in higher latent codes that lower latent codes were not able to encode. This enables preserving image details in reconstruction. To achieve high-quality editing, we learn how to transform the residual features for adapting to manipulations in latent codes. We train the framework to extract residual features and transform them via a novel architecture pipeline and cycle consistency losses. We run extensive experiments and compare our method with state-of-the-art inversion methods. Qualitative metrics and visual comparisons show significant improvements. Code: https://github.com/hamzapehlivan/StyleRes

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