GAN Inversion: A Survey (2101.05278v5)

Abstract: GAN inversion aims to invert a given image back into the latent space of a pretrained GAN model, for the image to be faithfully reconstructed from the inverted code by the generator. As an emerging technique to bridge the real and fake image domains, GAN inversion plays an essential role in enabling the pretrained GAN models such as StyleGAN and BigGAN to be used for real image editing applications. Meanwhile, GAN inversion also provides insights on the interpretation of GAN's latent space and how the realistic images can be generated. In this paper, we provide an overview of GAN inversion with a focus on its recent algorithms and applications. We cover important techniques of GAN inversion and their applications to image restoration and image manipulation. We further elaborate on some trends and challenges for future directions.

Overview of GAN Inversion: A Survey

The paper "GAN Inversion: A Survey" serves as an extensive compendium of techniques and applications related to the inversion of Generative Adversarial Networks (GANs). This survey thoroughly examines the process of mapping real images into the latent space of a pretrained GAN, enabling various applications in image processing by leveraging the properties of the latent space.

Fundamental Concepts and Methodological Taxonomy

The primary task of GAN inversion is to recover the latent code of a given image so that it can be accurately reconstructed using a pretrained GAN model. Notably, this inversion is crucial for applying pre-trained models, such as StyleGAN or BigGAN, in real image editing, image restoration, and image manipulation tasks.

The paper categorizes GAN inversion methodologies into three main types:

1. Learning-based Methods: These require training an encoder network to map images directly to their corresponding latent codes. While efficient, they may lack accuracy in image reconstruction.
2. Optimization-based Methods: These involve finding the optimal latent code through iterative optimization, usually providing more accurate image reconstruction but at a higher computational cost.
3. Hybrid Methods: These combine both learning and optimization to balance efficiency and reconstruction fidelity.

Latent Space Analysis

A significant portion of the survey is dedicated to analyzing the latent spaces of GANs, particularly how different latent spaces impact the efficacy of GAN inversion. The survey probes spaces such as $\mathcal{Z}$, $\mathcal{W}$, $\mathcal{W}^{+}$, $\mathcal{S}$, and $\mathcal{P}$, with the latter offering transformations that aid in more robust inversion outcomes.

Evaluation Metrics

The fidelity and photorealism of reconstructed images are key metrics for evaluating GAN inversion. The paper outlines popular metrics such as PSNR, SSIM, IS, and FID, providing a comprehensive perspective on how to assess both the quality and accuracy of GAN-generated imagery.

Applications and Implications

The survey articulates several applications made feasible through GAN inversion:

• Image Manipulation: Allows attribute editing and region-specific modifications.
• Image Restoration: Offers solutions for inpainting, denoising, and super-resolution.
• Latent Space Navigation: Enables discovery of interpretable and disentangled directions for semantic control.

The inversion techniques also display out-of-distribution generalizability, an essential trait for handling images not akin to the training datasets, hence expanding the GAN application scope.

Challenges and Future Directions

The paper identifies several challenges that remain, including the need for better theoretical understanding, domain generalization, precise control for fine-grained editing, and the extension of GAN inversion to other data modalities like audio and text. The exploration of new evaluation metrics to better assess the latent codes and perceptual quality also surfaces as a vital area for future research.

Conclusion

Overall, the survey provides an insightful and methodical assessment of GAN inversion, spotlighting its significance in advancing the capabilities of GANs beyond pure image generation. It opens pathways for future research to address existing limitations and unexplored dimensions, thereby contributing towards more versatile and comprehensive generative models.