In-Domain GAN Inversion for Real Image Editing (2004.00049v3)

Abstract: Recent work has shown that a variety of semantics emerge in the latent space of Generative Adversarial Networks (GANs) when being trained to synthesize images. However, it is difficult to use these learned semantics for real image editing. A common practice of feeding a real image to a trained GAN generator is to invert it back to a latent code. However, existing inversion methods typically focus on reconstructing the target image by pixel values yet fail to land the inverted code in the semantic domain of the original latent space. As a result, the reconstructed image cannot well support semantic editing through varying the inverted code. To solve this problem, we propose an in-domain GAN inversion approach, which not only faithfully reconstructs the input image but also ensures the inverted code to be semantically meaningful for editing. We first learn a novel domain-guided encoder to project a given image to the native latent space of GANs. We then propose domain-regularized optimization by involving the encoder as a regularizer to fine-tune the code produced by the encoder and better recover the target image. Extensive experiments suggest that our inversion method achieves satisfying real image reconstruction and more importantly facilitates various image editing tasks, significantly outperforming start-of-the-arts.

In-Domain GAN Inversion for Real Image Editing

The paper "In-Domain GAN Inversion for Real Image Editing" introduces a novel approach to GAN inversion, addressing the challenge of semantically meaningful image editing using GANs. Existing methods often emphasize pixel-level image reconstruction, neglecting the semantic alignment of the inverted code. This work proposes an innovative pipeline to enhance GAN inversion by ensuring the inverted latent code preserves the semantic structure of the original GAN latent space.

Problem and Approach

GAN inversion serves to reverse the image generation process, mapping a given image back into the latent space of a GAN model. Common methods—either encoder-based or optimization-based—often fail to maintain semantic significance in the inverted code. This misalignment complicates tasks like semantic image manipulation. The paper tackles this issue by proposing an "in-domain" inversion strategy, aiming to recover the input images both pixel-wise and semantically.

The authors developed a domain-guided encoder to map images directly into the latent space, ensuring semantic congruence. This initial mapping is further refined through domain-regularized optimization, fine-tuning the latent code while preserving the latent space's semantic properties.

Key Components

1. Domain-Guided Encoder: This encoder is trained not just on synthesized images but also on real images. During training, the reconstructed output image serves as supervision, allowing the generator's semantic knowledge to guide the encoder. This approach integrates adversarial training using the GAN's discriminator to ensure realistic outputs.
2. Domain-Regularized Optimization: This step starts with the encoder-produced code, optimizing it further with guidance from the encoder to maintain semantic integrity. Unlike typical pixel-focused optimization, this technique balances reconstruction fidelity with semantic preservation.

Experiments and Results

The paper presents extensive qualitative and quantitative results demonstrating the superiority of this technique over established inversion methods like Image2StyleGAN. Evaluation metrics include image interpolation, semantic manipulation, and a novel task, semantic diffusion.

• Semantic Preservation: Precision-recall curves for attribute classification (e.g., age, gender) using inverted codes highlight improved semantic alignment compared to previous methods.
• Reconstruction Quality: The proposed method achieves better reconstruction fidelity (as quantified by FID, SWD, and MSE metrics) than traditional approaches.
• Image Editing: In-domain inversion enables more coherent and realistic image interpolation and manipulation across various attributes (e.g., facial attributes, tower scenes). The results showcase smoother transitions and more robust semantic edits.
• Semantic Diffusion: Novel diffusion tasks showcase the method's capability to contextually integrate features from one image into another while preserving salient target features (like identity in faces).

Implications and Future Work

This research advances understanding of GAN latent spaces and enhances real-world applicability of GANs for image editing. It highlights the necessity for semantic awareness in inversion methods, offering a pathway to more sophisticated editing and manipulation tools.

Future research could explore broader applications across different GAN architectures, test the approach's scalability, and refine semantic understanding further. Integrating these inversion techniques into broader generative models might also unlock new avenues for realistic content creation.

In conclusion, the paper delivers a robust framework for GAN inversion, enhancing both reconstruction quality and semantic interpretability—paving the way for more effective real-world image editing applications.