Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANsDownload PDF

29 Sept 2021, 00:31 (edited 07 May 2022)ICLR 2022 PosterReaders: Everyone
  • Keywords: generative adversarial network, disentanglement, semantic factorization, latent space control, image manipulation, grassmannian
  • Abstract: The discovery of the disentanglement properties of the latent space in GANs motivated a lot of research to find the semantically meaningful directions on it. In this paper, we suggest that the disentanglement property is closely related to the geometry of the latent space. In this regard, we propose an unsupervised method for finding the semantic-factorizing directions on the intermediate latent space of GANs based on the local geometry. Intuitively, our proposed method, called $\textit{Local Basis}$, finds the principal variation of the latent space in the neighborhood of the base latent variable. Experimental results show that the local principal variation corresponds to the semantic factorization and traversing along it provides strong robustness to image traversal. Moreover, we suggest an explanation for the limited success in finding the global traversal directions in the latent space, especially $\mathcal{W}$-space of StyleGAN2. We show that $\mathcal{W}$-space is warped globally by comparing the local geometry, discovered from Local Basis, through the metric on Grassmannian Manifold. The global warpage implies that the latent space is not well-aligned globally and therefore the global traversal directions are bound to show limited success on it.
  • One-sentence Summary: We propose a method for finding local-geometry-aware traversal directions on the intermediate latent space of Generative Adversarial Networks (GANs).
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