Recently, StyleGAN has enabled various image manipulation and editing tasks thanks to the high-quality generation and the disentangled latent space. However, additional architectures or task-specific training paradigms are usually required for different tasks. In this work, we take a deeper look at the spatial properties of StyleGAN. We show that with a pretrained StyleGAN along with some operations, without any additional architecture, we can perform comparably to the state-of-the-art methods on various tasks, including image blending, panorama generation, generation from a single image, controllable and local multimodal image to image translation, and attributes transfer. The proposed method is simple, effective, efficient, and applicable to any existing pretrained StyleGAN model.

翻译：最近,StyleGAN通过高质量的生成和分解的潜在空间,使各种图像操作和编辑任务得以完成,然而,不同任务通常需要额外的架构或任务特定培训模式。在这项工作中,我们更深入地审视StyleGAN的空间特性。我们显示,通过预先培训的StyleGAN以及一些操作,在没有附加结构的情况下,我们可以在各种任务上执行与最先进的方法相当的工作,包括图像混合、全景生成、从单一图像生成、可控和本地多式图像生成到图像翻译和属性转移。提议的方法简单、有效、高效,并适用于任何现有的经过培训的StyGAN模型。