StyleGAN of All Trades: Image Manipulation with Only Pretrained StyleGAN

• URL: http://arxiv.org/abs/2111.01619v1
• Date: Tue, 2 Nov 2021 14:31:22 GMT
• Title: StyleGAN of All Trades: Image Manipulation with Only Pretrained StyleGAN
• Authors: Min Jin Chong, Hsin-Ying Lee, David Forsyth
• Abstract summary: We show that with a pretrained StyleGAN along with some operations, we can perform comparably to the state-of-the-art methods on various tasks. The proposed method is simple, effective, efficient, and applicable to any existing pretrained StyleGAN model.
• Score: 17.93566359555703
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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