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arXiv · 2111.01619

StyleGAN of All Trades: Image Manipulation with Only Pretrained StyleGAN

Min Jin Chong·Hsin-Ying Lee·David Forsyth
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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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Min Jin Chong, Hsin-Ying Lee, David Forsyth. 2021-11-02. StyleGAN of All Trades: Image Manipulation with Only Pretrained StyleGAN. https://arxiv.org/abs/2111.01619

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