Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation

Abstract: We present a generic image-to-image translation framework, pixel2style2pixel (pSp). Our pSp framework is based on a novel encoder network that directly generates a series of style vectors which are fed into a pretrained StyleGAN generator, forming the extended W+ latent space. We first show that our encoder can directly embed real images into W+, with no additional optimization. Next, we propose utilizing our encoder to directly solve image-to-image translation tasks, defining them as encoding problems from some input domain into the latent domain. By deviating from the standard invert first, edit later methodology used with previous StyleGAN encoders, our approach can handle a variety of tasks even when the input image is not represented in the StyleGAN domain. We show that solving translation tasks through StyleGAN significantly simplifies the training process, as no adversary is required, has better support for solving tasks without pixel-to-pixel correspondence, and inherently supports multi-modal synthesis via the resampling of styles. Finally, we demonstrate the potential of our framework on a variety of facial image-to-image translation tasks, even when compared to state-of-the-art solutions designed specifically for a single task, and further show that it can be extended beyond the human facial domain.

• The paper presents the novel pSp framework that uses a dedicated encoder to embed images directly into StyleGAN’s W+ latent space for high-fidelity translation.
• The approach streamlines image-to-image tasks—such as inpainting, super-resolution, and frontalization—by bypassing traditional, time-consuming optimization methods.
• Quantitative results show that pSp outperforms existing methods in reconstruction accuracy and computational efficiency, making it a unified solution for diverse synthesis challenges.

A StyleGAN Encoder for Image-to-Image Translation: Overview and Insights

The paper "Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation" by Elad Richardson et al. introduces the pixel2style2pixel (pSp) framework, a novel approach for performing image-to-image translation using a pretrained StyleGAN generator through a specialized encoder network. The core innovation lies in the ability to directly map input images into the $\mathcal{W+}$ latent space of StyleGAN, facilitating a spectrum of image translation tasks in a unified manner.

Framework Development and Key Achievements

The pSp framework operates by integrating a novel encoder network optimized to generate style vectors coherent with those utilized by StyleGAN. These style vectors are subsequently fed into the pretrained StyleGAN generator, which forms the basis for the $\mathcal{W+}$ latent space. This approach allows pSp to bypass the conventional "invert first, edit later" methodology by enabling direct embedding of real images into $\mathcal{W+}$ without the need for additional optimization processes.

Significant milestones include:

1. Encoder Performance: The proposed encoder can directly embed images into the $\mathcal{W+}$ space with high fidelity, outperforming existing methods both in terms of reconstruction accuracy and speed.
2. Versatile Image Translation: By framing image translation tasks as encodings from input images to latent domains, pSp demonstrates proficiency across various tasks such as style inversion, facial frontalization, inpainting, and super-resolution.
3. Multi-Modal Synthesis: Leveraging the style mixing properties inherent in StyleGAN, pSp supports multi-modal image synthesis directly, thereby simplifying the architecture and training process while enhancing the quality of ambiguous tasks like sketch-to-image generation.

Numerical Results and Comparative Analysis

Quantitatively, the pSp framework exhibits superior performance metrics. For example, in the StyleGAN inversion task, it significantly outperforms existing methods such as ALAE and IDInvert, showing higher LPIPS scores and lower MSE, with a runtime metric that asserts its computational efficiency. The similarity score evaluated via CurricularFace demonstrates a considerable leap in identity preservation in reconstructed images. This is visually corroborated by high-quality reconstruction results in comparison to alternative techniques.

In facial frontalization, the human perceptual similarity evaluations score pSp slightly below the RotateAndRender (R{content}R) method, yet pSp compensates by offering a more streamlined approach with higher computational efficiency. This divergence underscores the practical utility and straightforward training regimen inherent to pSp, which crucially avoids the need for complex steps like geometric 3D alignment.

Implications and Theoretical Contributions

The pSp framework contributes both practically and theoretically to the field of image-to-image translation. Practically, its unified architecture simplifies the deployment of models capable of handling diverse translation tasks without requiring task-specific model redesigns. Theoretically, by directly encoding inputs into $\mathcal{W+}$ and facilitating multi-modal outputs naturally, pSp challenges previous paradigms requiring pixel-to-pixel correspondence, thereby demonstrating feasibility for more global, non-local transformations.

Speculative future directions could include extending the framework to more complex domains beyond human faces, as initial experiments on domains such as the AFHQ Cat and Dog datasets show promising adaptability. Further optimization of the style-mixing approach and refinement of the encoder's architectural components could enhance the quality and diversity of synthesized outputs even further.

Conclusion

The "Encoding in Style" paper presents a compelling advancement in image-to-image translation, rooted in the integration of a robust encoder with the powerful StyleGAN generator. The pSp framework's ability to handle various tasks through a unified approach marks a significant leap in effectively utilizing generative models for practical and diverse image transformation tasks. By simplifying the training process and eliminating task-specific architectural complexities, pSp stands as an influential model with broad implications for future research and applications in AI-driven image synthesis.