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.

本文介绍了一种简单的无监督方法，用于提供训练良好的局部潜在子空间，使得通过潜在编码的导航能够保持生成图像的真实感。实验证明，在局部潜在子空间内生成的图像能够在潜在编码反复调整的情况下保持真实感，并且这种方法对各种类型的基于风格的模型中的潜在编码优化都具有应用潜力。