Designing an Encoder for StyleGAN Image Manipulation (2102.02766v1)

Abstract: Recently, there has been a surge of diverse methods for performing image editing by employing pre-trained unconditional generators. Applying these methods on real images, however, remains a challenge, as it necessarily requires the inversion of the images into their latent space. To successfully invert a real image, one needs to find a latent code that reconstructs the input image accurately, and more importantly, allows for its meaningful manipulation. In this paper, we carefully study the latent space of StyleGAN, the state-of-the-art unconditional generator. We identify and analyze the existence of a distortion-editability tradeoff and a distortion-perception tradeoff within the StyleGAN latent space. We then suggest two principles for designing encoders in a manner that allows one to control the proximity of the inversions to regions that StyleGAN was originally trained on. We present an encoder based on our two principles that is specifically designed for facilitating editing on real images by balancing these tradeoffs. By evaluating its performance qualitatively and quantitatively on numerous challenging domains, including cars and horses, we show that our inversion method, followed by common editing techniques, achieves superior real-image editing quality, with only a small reconstruction accuracy drop.

• The paper presents e4e, an encoder designed for effective real-image inversion in StyleGAN that balances distortion and editability.
• It analyzes the latent space of StyleGAN, showing how proximity to the W space improves image quality during editing.
• Empirical evaluations across diverse datasets confirm that e4e enhances perceptual quality and editability with minimal distortion.

Designing an Encoder for StyleGAN Image Manipulation

The paper "Designing an Encoder for StyleGAN Image Manipulation" presents an in-depth study of the StyleGAN latent space to enhance image editing techniques. The authors propose a novel encoder termed e4e (Encoder for Editing) that balances the tradeoff between image distortion, perceptual quality, and editability, enabling effective real-image manipulations.

Motivation

Generative Adversarial Networks (GANs), particularly StyleGAN, have achieved significant milestones in unconditional image synthesis, showcasing exceptional image realism and manipulation capabilities. However, editing real images using these models remains challenging due to the necessity of accurate image inversion into the GAN's latent space. This paper addresses this challenge by analyzing the structure of StyleGAN's latent space and proposing a method that allows high-quality inversion while preserving editability.

Key Contributions

1. Latent Space Analysis:
   • The authors dissect the StyleGAN latent space, identifying and analyzing two key tradeoffs: distortion-editability and distortion-perception.
   • They demonstrate that proximity to the intermediate latent space $\mathcal{W}$, compared to its extension $\mathcal{W}^k$ or $\mathcal{W}^+,$ influences the quality and editability of representations.
2. Encoder Design Principles:
   • The authors put forth two principles for designing encoders: minimizing variation between the inferred style codes and minimizing deviation from the true distribution of $\mathcal{W}$.
   • By implementing these principles, the paper presents e4e, an encoder tailored to invert images close to $\mathcal{W}$, enhancing perceptual quality and editability at a slight distortion cost.
3. Progressive Training Scheme:
   • A unique progressive training scheme that gradually increases the variance between the style vectors during training, maintaining proximity to $\mathcal{W}$ and ensuring high expressive power.
4. Empirical Validation:
   • Extensive qualitative and quantitative evaluations across diverse domains (faces, cars, horses, cats, and churches) validate the effectiveness of the proposed method.
   • The paper demonstrates significant improvements in editability and perceptual quality using e4e, with only a minor tradeoff in distortion.

Experimental Setup and Results

The authors use datasets such as FFHQ, CelebA-HQ, Stanford Cars, and LSUN to train and test the proposed encoder. They implement various editing algorithms like StyleFlow, InterFaceGAN, GANSpace, and SeFa for evaluating editability. The empirical results consistently show that encodings closer to $\mathcal{W}$ provide superior perceptual quality and more realistic edits.

Main Findings

• Distortion vs. Perceptual Quality: The study finds that lower variance between style vectors results in higher perceptual quality, demonstrated through empirical results and user studies. This confirms the theoretical distortion-perception tradeoff.
• Distortion vs. Editability: Proximity to $\mathcal{W}$ significantly enhances the editability of images, as edits on these representations yield more realistic and semantically meaningful results.

Evaluation Metrics

To objectively assess the performance, several metrics are employed:

• Distortion: Using $L_2$ and LPIPS metrics to quantify per-image similarity.
• Perceptual Quality: Evaluated using FID and SWD, with user studies providing subjective validation.
• Editability: Investigated through FID and SWD metrics on edited images, alongside the novel Latent Editing Consistency (LEC) measure.

Implications and Future Work

The proposed e4e encoder paves the way for more efficient and robust real-image editing using StyleGAN. By refining the inversion process, this approach enhances the practical applications of GANs in image manipulation tasks. The study also sets the stage for further research in multi-modal generators and fine-tuning techniques that could boost the performance of GAN-based inversion and editing.

Conclusion

The paper successfully addresses the critical challenge of image inversion in StyleGAN and offers a well-founded solution that balances distortion, perceptual quality, and editability. The proposed e4e encoder exhibits substantial improvements in the quality of real-image editing, making it a valuable contribution to the field of image manipulation within the framework of GAN-generated latent spaces. Future research will likely build upon these findings, exploring even more sophisticated models and techniques for advanced image synthesis and editing.