Health, Americas, Special Report

[AprpdlNuh]

In light of the translation and rotation equivariance provided by StyleGAN3, we also examine the differences between generators trained on aligned and on unaligned data. Surprisingly, we observe that both kinds of generators are comparable in terms of their ability to generate unaligned images and control their position and rotation. However, we find that using the aligned generators is preferable for tasks such as disentangled editing and inversion. Applying the insights gained in our analysis and experiments over still images, we propose a novel workflow for inverting and editing real videos using StyleGAN3. Notably, we leverage the capabilities of StyleGAN3 to reduce texture sticking and expand the field of view when working on a video with a cropped subject. In this work, we experiment with the image manipulation techniques from Shen et al. To manipulate real images they must first be projected into one of the StyleGAN latent spaces. We refer the reader to the exposition in Xia et al. GAN inversion. The applications it enables.




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