Optimizing Latent Space Directions For GAN-based Local Image Editing

• URL: http://arxiv.org/abs/2111.12583v1
• Date: Wed, 24 Nov 2021 16:02:46 GMT
• Title: Optimizing Latent Space Directions For GAN-based Local Image Editing
• Authors: Ehsan Pajouheshgar, Tong Zhang, Sabine S\"usstrunk
• Abstract summary: We present a novel objective function to evaluate the locality of an image edit. Our framework, called Locally Effective Latent Space Direction (LELSD), is applicable to any dataset and GAN architecture. Our method is also computationally fast and exhibits a high extent of disentanglement, which allows users to interactively perform a sequence of edits on an image.
• Score: 15.118159513841874
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Generative Adversarial Network (GAN) based localized image editing can suffer ambiguity between semantic attributes. We thus present a novel objective function to evaluate the locality of an image edit. By introducing the supervision from a pre-trained segmentation network and optimizing the objective function, our framework, called Locally Effective Latent Space Direction (LELSD), is applicable to any dataset and GAN architecture. Our method is also computationally fast and exhibits a high extent of disentanglement, which allows users to interactively perform a sequence of edits on an image. Our experiments on both GAN-generated and real images qualitatively demonstrate the high quality and advantages of our method.

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