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。

训练的潜在空间导航：在基于样式的模型生成的图像中预防缺乏逼真感