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Overparameterization Improves StyleGAN Inversion
[article]
2022
arXiv
pre-print
Deep generative models like StyleGAN hold the promise of semantic image editing: modifying images by their content, rather than their pixel values. Unfortunately, working with arbitrary images requires inverting the StyleGAN generator, which has remained challenging so far. Existing inversion approaches obtain promising yet imperfect results, having to trade-off between reconstruction quality and downstream editability. To improve quality, these approaches must resort to various techniques that
arXiv:2205.06304v1
fatcat:lrcvul44pvehrk37mgmo6ng2gu