In case you didn't read it yet, I used a StyleGAN to interpolate between a few popular characters from HBO's Game of Thrones series.
Here's something interesting to note: All the results (images and animations) were generated from Nvidia's StyleGAN that was pretrained on the FFHQ dataset, with absolutely no fine-tuning.
Instead, to make StyleGAN work for Game of Thrones characters, I used another model that maps images onto StyleGAN's latent space. I gave it images of Jon, Daenerys, Jaime, etc. and got latent vectors that when fed through StyleGAN, recreate the original image.
With the latent vectors for the images in hand, it's really to modify them in all the ways described in the StyleGAN paper (style mixing, interpolations, etc.) as well as through simple arithmetic in the latent space (such as shifting the latent vector in the "smiling direction").
As a bonus, since there's no StyleGAN training involved, all the steps that I just mentioned can be executed extremely fast.
In case you didn't read it yet, I used a StyleGAN to interpolate between a few popular characters from HBO's Game of Thrones series.
Here's something interesting to note: All the results (images and animations) were generated from Nvidia's StyleGAN that was pretrained on the FFHQ dataset, with absolutely no fine-tuning.
Instead, to make StyleGAN work for Game of Thrones characters, I used another model that maps images onto StyleGAN's latent space. I gave it images of Jon, Daenerys, Jaime, etc. and got latent vectors that when fed through StyleGAN, recreate the original image.
With the latent vectors for the images in hand, it's really to modify them in all the ways described in the StyleGAN paper (style mixing, interpolations, etc.) as well as through simple arithmetic in the latent space (such as shifting the latent vector in the "smiling direction").
As a bonus, since there's no StyleGAN training involved, all the steps that I just mentioned can be executed extremely fast.