  Recent studies on StyleGAN variants show promising performances for various
generation tasks. In these models, latent codes have traditionally been
manipulated and searched for the desired images. However, this approach
sometimes suffers from a lack of photorealism in generated images due to a lack
of knowledge about the geometry of the trained latent space. In this paper, we
show a simple unsupervised method that provides well-trained local latent
subspace, enabling latent code navigation while preserving the photorealism of
the generated images. Specifically, the method identifies densely mapped latent
spaces and restricts latent manipulations within the local latent subspace.
Experimental results demonstrate that images generated within the local latent
subspace maintain photorealism even when the latent codes are significantly and
repeatedly manipulated. Moreover, experiments show that the method can be
applied to latent code optimization for various types of style-based models.
Our empirical evidence of the method will benefit applications in style-based
models.
