High-Fidelity GAN Inversion for Image Attribute Editing (2109.06590v4)

Abstract: 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.

High-Fidelity GAN Inversion for Image Attribute Editing

This paper presents a novel framework for high-fidelity generative adversarial network (GAN) inversion, enabling precise image attribute editing while maintaining image-specific details such as background, appearance, and illumination. The proposed approach addresses the challenges in high-fidelity GAN inversion through the lens of lossy data compression, focusing on overcoming the limitations of previous methods that often fail to preserve high-frequency details due to low bit-rate latent codes.

Challenges in GAN Inversion

Conventional GAN inversion methods struggle with a trade-off between reconstruction fidelity and editability. Increasing the latent code's size might enhance fidelity but often at the expense of editability. The paper introduces a distortion consultation approach, projecting a distortion map into a high-rate latent map to complement basic low-rate latent codes, effectively enhancing details while retaining editability.

Proposed Framework

The core innovation lies in the distortion consultation inversion (DCI) and an adaptive distortion alignment (ADA) module. The DCI uses a distortion map from the initial low-fidelity reconstruction to recover image-specific details. This is achieved by projecting the distortion map onto a higher-rate latent map, enhancing the fidelity of the reconstructed image. The ADA module aligns distortion information between edited and inverted images, bridging gaps through a self-supervised training scheme.

Experimental Results

Experiments in face and car image domains demonstrate substantial improvements in both inversion and editing quality, highlighting the framework's ability to preserve intricate details. Notably, the proposed method shows robustness to variations in viewpoint and illumination, offering temporally consistent editing for video applications.

Implications and Future Work

Practically, this work allows for more accurate and detailed image manipulations, which can significantly benefit areas requiring high fidelity in visual content creation and modification, such as digital art, visual effects in films, and virtual reality content. Theoretically, it opens avenues for further exploration into the trade-offs in GAN inversion and the potential for integrating similar techniques in other machine learning applications requiring high-resolution outputs.

The potential future developments include extending the framework to broader image domains and exploring further optimization for real-time applications. As with any novel approach, further research could explore integrating automated alignment and distortion handling techniques, enhancing stability and robustness under diverse conditions.

In conclusion, this paper provides a substantial advancement in GAN inversion methodologies, balancing between fidelity and editability through innovative use of distortion consultation, offering valuable insights and tools for both academic exploration and practical application in image attribute editing.