WarpedGANSpace: Finding non-linear RBF paths in GAN latent space

TL;DR

This paper introduces WarpedGANSpace, a method for discovering non-linear, interpretable paths in GAN latent spaces using RBF-based warpings, improving control over generated image variations.

Contribution

It proposes a novel non-linear warping approach in GAN latent space, extending linear path methods, and demonstrates improved disentanglement and interpretability of image transformations.

Findings

Non-linear paths produce more disentangled image changes.

The method outperforms state-of-the-art linear approaches.

Code and models are publicly available.

Abstract

This work addresses the problem of discovering, in an unsupervised manner, interpretable paths in the latent space of pretrained GANs, so as to provide an intuitive and easy way of controlling the underlying generative factors. In doing so, it addresses some of the limitations of the state-of-the-art works, namely, a) that they discover directions that are independent of the latent code, i.e., paths that are linear, and b) that their evaluation relies either on visual inspection or on laborious human labeling. More specifically, we propose to learn non-linear warpings on the latent space, each one parametrized by a set of RBF-based latent space warping functions, and where each warping gives rise to a family of non-linear paths via the gradient of the function. Building on the work of Voynov and Babenko, that discovers linear paths, we optimize the trainable parameters of the set of RBFs, so as that images that are generated by codes along different paths, are easily distinguishable by a discriminator network. This leads to easily distinguishable image transformations, such as pose and facial expressions in facial images. We show that linear paths can be derived as a special case of our method, and show experimentally that non-linear paths in the latent space lead to steeper, more disentangled and interpretable changes in the image space than in state-of-the art methods, both qualitatively and quantitatively. We make the code and the pretrained models publicly available at: https://github.com/chi0tzp/WarpedGANSpace.