5/30/2023 0 Comments Neooffice image editing![]() The framework allows us to learn an arbitrary number of editing vectors, which can then be directly applied on other images at interactive rates. To amortize optimization, we find “editing vectors” in latent space that realize the edits. Specifically, we embed an image into the GAN’s latent space and perform conditional latent code optimization according to the segmentation edit, which effectively also modifies the image. EditGAN builds on a GAN framework that jointly models images and their semantic segmentations ( DatasetGAN), requiring only a handful of labeled examples – making it a scalable tool for editing. Here, we propose EditGAN, a novel method for high-quality, high-precision semantic image editing, allowing users to edit images by modifying their highly detailed part segmentation masks, e.g., drawing a new mask for the headlight of a car. However, most GAN-based image editing methods often require large-scale datasets with semantic segmentation annotations for training, only provide high level control, or merely interpolate between different images. Generative adversarial networks (GANs) have recently found applications in image editing. (4) Users can perform editing simply by applying previously learnt editing vectors and manipulate images at interactive rates. (2 & 3) Users can modify segmentation masks, based on which we perform optimization in the GAN’s latent space to realize the edit. (1) EditGAN builds on a GAN framework that jointly models images and their semantic segmentations.
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