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(1/6) Excited to share our #NVIDIAAI #NeurIPS2021 paper “EditGAN: High-Precision Semantic Image Editing” (nv-tlabs.github.io/editGAN/), achieving an unprecedented level of detail in GAN-based image editing!
w/ @karsten_kreis , @lidaiqing , @seungkim0123 , @abtorralba , @FidlerSanja
w/ @karsten_kreis , @lidaiqing , @seungkim0123 , @abtorralba , @FidlerSanja
(2/6) EditGAN builds on a GAN framework that jointly models images and their semantic segmentation (nv-tlabs.github.io/datasetGAN/). Manually modifying segmentations is easy. This allows us to find editing vectors in latent space that enable high-precision image editing. 
(3/6) EditGAN allows us to learn an arbitrary number of editing vectors, which can be directly applied to other images at interactive rates. We show that EditGAN can manipulate images with an unprecedented level of detail and freedom while preserving full image quality.
