Rewriting a Deep Generative Model, ECCV 2020 (oral). Interactive tool to directly edit the rules of a GAN to synthesize scenes with objects added, removed, or altered. Change StyleGANv2 to make extravagant eyebrows, or horses wearing hats.
Hi David, thanks for the great work so far. I trained a custom dataset with StyleGAN2 and converted the weight to pytorch. Then I tried on rewriting some of the features, but it proves to be quite challenging.
For example, here I tried to add an extra bottle holder, but it only changed very few of the output.
And here, I tried changing the back wheel to black, but it changed both wheels instead, and although it changed most of the output, some are not.
I also tried changing some of the other features such as the frame shape and the seat location but failed to give the changes I wanted. I assume this is because I do not choose the best layer to dissect or the wrong value for rank update and the learning parameter.
My question is, how do you choose the best layer and rank update for your previous sample? Especially if I don't have any segmentation labels for the object.
It'd be great if you can make a Colab tutorial on dissecting the network and finding the best layer and other parameters for the features that we selected.
Hi David, thanks for the great work so far. I trained a custom dataset with StyleGAN2 and converted the weight to pytorch. Then I tried on rewriting some of the features, but it proves to be quite challenging.
For example, here I tried to add an extra bottle holder, but it only changed very few of the output.
And here, I tried changing the back wheel to black, but it changed both wheels instead, and although it changed most of the output, some are not.
I also tried changing some of the other features such as the frame shape and the seat location but failed to give the changes I wanted. I assume this is because I do not choose the best layer to dissect or the wrong value for rank update and the learning parameter.
My question is, how do you choose the best layer and rank update for your previous sample? Especially if I don't have any segmentation labels for the object.
It'd be great if you can make a Colab tutorial on dissecting the network and finding the best layer and other parameters for the features that we selected.
Thanks,