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View Code? Open in Web Editor NEW[ICCV 2023] Scenimefy: Learning to Craft Anime Scene via Semi-Supervised Image-to-Image Translation
Home Page: https://yuxinn-j.github.io/projects/Scenimefy.html
License: Other
[ICCV 2023] Scenimefy: Learning to Craft Anime Scene via Semi-Supervised Image-to-Image Translation
Home Page: https://yuxinn-j.github.io/projects/Scenimefy.html
License: Other
Could you please tell me how to train your model for this code?
Where can I get your test dataset you mentioned in your paper?
Thanks for your work,
As you say that the high-quality anime scene dataset comprises 5,958 images, but I follow the Scenimefy/Anime_dataset/README.md, obtaining 5151 cartoon images. If I were to manually filter the dataset again, the number of images used for training would be even smaller. Why did you get 5958 images.
From the project web, those demo videos look very stable and amazing. How to process a video? Just extract a video to frames and process frames one by one? Or there is other stability tech?
Hi! I wonder how long does it take for one group of experiment? Thanks!
In the README there are the instructions on how to train a custom model. It says to run train.py
, but this file is missing.
Hello,
I am trying to replicate your project, but I encountered an issue with downloading the movie data ''The Place Promised in Our Early Days''. It seems that I have no access rights on Google Drive. Could you please check the sharing settings or provide an alternative link to access the file?
Thank you for your assistance!
Hello, I have a question: why are real_A and real_B spliced together when training unpaired images, but there is no such step when training paired images?
"""
Run forward pass; called by both functions <optimize_parameters> and ."""
self.real = torch.cat((self.real_A, self.real_B),
dim=0) if self.opt.dce_idt and self.opt.isTrain else self.real_A
if self.opt.flip_equivariance:
self.flipped_for_equivariance = self.opt.isTrain and (np.random.random() < 0.5)
if self.flipped_for_equivariance:
self.real = torch.flip(self.real, [3])
# forward unpaired data
self.fake = self.netG(self.real)
self.fake_B = self.fake[:self.real_A.size(0)]
if self.opt.dce_idt:
self.idt_B = self.fake[self.real_A.size(0):]
# forward paired data
self.fake_p = self.netG(self.real_A_p)
self.fake_B_p = self.fake_p[:self.real_A_p.size(0)]
Hello 👋 very interesting work! Do you have any plans to release StyleGAN finetuning scripts?
Hi, thank you for your greate work! But I don't find where the StylePatchNCE loss, can you help me? I'm looking forward your repying.
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