Pivotal Tuning for Latent-based Editing of Real Images (2106.05744v1)

Abstract: Recently, a surge of advanced facial editing techniques have been proposed that leverage the generative power of a pre-trained StyleGAN. To successfully edit an image this way, one must first project (or invert) the image into the pre-trained generator's domain. As it turns out, however, StyleGAN's latent space induces an inherent tradeoff between distortion and editability, i.e. between maintaining the original appearance and convincingly altering some of its attributes. Practically, this means it is still challenging to apply ID-preserving facial latent-space editing to faces which are out of the generator's domain. In this paper, we present an approach to bridge this gap. Our technique slightly alters the generator, so that an out-of-domain image is faithfully mapped into an in-domain latent code. The key idea is pivotal tuning - a brief training process that preserves the editing quality of an in-domain latent region, while changing its portrayed identity and appearance. In Pivotal Tuning Inversion (PTI), an initial inverted latent code serves as a pivot, around which the generator is fined-tuned. At the same time, a regularization term keeps nearby identities intact, to locally contain the effect. This surgical training process ends up altering appearance features that represent mostly identity, without affecting editing capabilities. We validate our technique through inversion and editing metrics, and show preferable scores to state-of-the-art methods. We further qualitatively demonstrate our technique by applying advanced edits (such as pose, age, or expression) to numerous images of well-known and recognizable identities. Finally, we demonstrate resilience to harder cases, including heavy make-up, elaborate hairstyles and/or headwear, which otherwise could not have been successfully inverted and edited by state-of-the-art methods.

• The paper introduces the Pivotal Tuning Inversion (PTI) methodology that enhances image editing by adjusting the pretrained generator to maintain identity and editability.
• It employs a two-step process with GAN inversion into StyleGAN's latent space followed by localized tuning using a pivotal latent code.
• Quantitative improvements in LPIPS, MSE, MS-SSIM, and identity similarity highlight PTI's superior performance and its potential for real-time and extended generative applications.

Pivotal Tuning for Latent-based Editing of Real Images

The paper "Pivotal Tuning for Latent-based Editing of Real Images" tackles the significant challenge of editing facial images using StyleGAN, particularly when dealing with out-of-domain images. StyleGAN's latent space has shown inherent limitations due to the distortion-editability trade-off, where maintaining the original image's identity while providing meaningful edits is problematic. This research introduces an innovative methodology to mitigate this trade-off via a process termed Pivotal Tuning Inversion (PTI).

Summary of Methodology

The authors propose a two-step process. Initially, they perform GAN inversion by projecting a real image into StyleGAN's native latent space, $\mathcal{W}$. This inversion step aims to find an editable latent code while capturing significant features of the original image, avoiding extended latent spaces like $\mathcal{W+}$ that are known to induce less editable representations.

Following inversion, Pivotal Tuning is applied. This involves slight adjustments to the pretrained generator to ensure that the inverted code generates an image closely matching the original input. The process revolves around a pivotal latent code, which is optimized without modifying the code itself, ensuring the preservation of editability. Notably, this approach includes a regularization term to localize the impact of tuning, minimizing unintended alterations elsewhere in the latent space.

Key Results

PTI demonstrates superior performance compared to state-of-the-art methods across both reconstruction and editing tasks. The method achieves high editability while maintaining identity fidelity, a significant advancement over previous solutions like $\mathcal{W+}$ embedding. The authors present quantitative improvements measured by LPIPS, MSE, MS-SSIM, and identity similarity metrics, showing marked enhancements in visual quality and editability.

Implications and Future Directions

This work has substantial practical implications for real image editing, particularly in applications that necessitate high fidelity and precise control over visual attributes of facial images. The PTI approach extends the utility of StyleGAN beyond its original scope, providing more flexible and robust editing capabilities for out-of-distribution images such as those with unique make-up or hairstyles.

Looking forward, the authors suggest potential expansions of this methodology. One direction includes developing a single-pass PTI process through a trainable mapper, thus enabling real-time applications. Another direction could involve extending PTI principles to other generative models like BigGAN.

Conclusion

"Pivotal Tuning for Latent-based Editing of Real Images" presents a technically sophisticated and impactful advancement in the domain of image editing using GANs. The introduction of PTI effectively bridges gaps in current methodologies, providing a robust solution to the distortion-editability trade-off while offering new avenues for future research and application in generative model personalization.