Americas, Special Report, World News

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StyleGAN architectures are notable for their semantically-rich, disentangled, and generally well-behaved latent spaces. When it comes to video processing, new challenges arise. Editing should not only be disentangled and realistic, but also temporally consistent across frames. For example, when interpolating within the latent space, the hair and face typically do not move in unison. Naturally, these unique properties make StyleGAN3 better suited for video processing than previous style-based generators. However, significant changes introduced in StyleGAN3 architecture raise many questions and new challenges. Central among these is the disentanglement of its latent spaces and the ability to accurately invert and edit real images. In this paper, we analyze StyleGAN3, aiming at understanding and exploring its capabilities and performance. Some of the questions we attempt to answer are: How does the disentanglement of the latent representations in StyleGAN3 compare to StyleGAN2? Do the techniques devised for identifying latent editing controls still work? We therefore examine how well these existing techniques can be adapted to achieve comparable performance with StyleGAN3, and, in particular, cope with the inversion of unaligned images.




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