Navigating the GAN Parameter Space for Semantic Image Editing (2011.13786v3)

Abstract: Generative Adversarial Networks (GANs) are currently an indispensable tool for visual editing, being a standard component of image-to-image translation and image restoration pipelines. Furthermore, GANs are especially useful for controllable generation since their latent spaces contain a wide range of interpretable directions, well suited for semantic editing operations. By gradually changing latent codes along these directions, one can produce impressive visual effects, unattainable without GANs. In this paper, we significantly expand the range of visual effects achievable with the state-of-the-art models, like StyleGAN2. In contrast to existing works, which mostly operate by latent codes, we discover interpretable directions in the space of the generator parameters. By several simple methods, we explore this space and demonstrate that it also contains a plethora of interpretable directions, which are an excellent source of non-trivial semantic manipulations. The discovered manipulations cannot be achieved by transforming the latent codes and can be used to edit both synthetic and real images. We release our code and models and hope they will serve as a handy tool for further efforts on GAN-based image editing.

Navigating the GAN Parameter Space for Semantic Image Editing

The scholarly work "Navigating the GAN Parameter Space for Semantic Image Editing" offers a significant contribution to the domain of computer vision, emphasizing a novel method for image editing via Generative Adversarial Networks (GANs). Unlike conventional approaches that typically involve latent space manipulation to achieve desired image transformations, this paper proposes navigating the parameter space of the generator itself. This strategy broadens the potential for non-trivial semantic manipulations that are not achievable through latent space alone.

The authors focus on StyleGAN2, a sophisticated GAN model, and illustrate how its parameter space contains interpretable directions for a wide array of visual effects. They employ straightforward techniques to discover these directions, which enable semantic edits both in synthetic and real images. Two primary methods are presented: an optimization-based approach and a spectrum-based approach. The optimization-based technique draws inspiration from unsupervised direction learning in latent space, optimizing a direction matrix and a reconstructor to identify efficient parameter shifts. The spectrum-based method leverages the eigenvectors of the Hessian of the LPIPS distance function with respect to the generator’s parameters, thus assessing perceptual changes.

The paper showcases that changes in the GAN's parameters can induce distinct transformations. These transformations span from altering object scales and aspect ratios to adjusting their geometrical and location-based properties. They demonstrate these effects across various datasets, including FFHQ, LSUN-Cars, LSUN-Horse, and LSUN-Church, providing compelling evidence of the method's versatility and efficacy.

Significantly, the experiments reveal that effects such as "nose length" or "wheel size" alterations, achieved through parameter shifts, are not replicable by manipulating latent codes or intermediate activations. Thus, this approach expands the arsenal of image editing techniques beyond the reach of traditional latent space manipulation.

Quantitative metrics such as Fréchet Inception Distance (FID) are employed to measure the impact of these parameter shifts on image realism, confirming high visual fidelity even with substantial transformations. Moreover, the framework can be employed on a variety of GAN architectures, suggesting its generalizability.

In conclusion, this research advances the capabilities of semantic image editing by demonstrating effective parameter space navigation techniques within GANs. By uncovering diverse and interpretable parameter directions, the work provides a new paradigm for the semantic manipulation of generated imagery, with potential implications for enhancing AI-driven editing applications. The findings prompt further exploration into parameter space manipulation across different GAN architectures, forecasting future developments in efficient and versatile image editing techniques.