AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation

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Deep Learning AniGAN
Overview

AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation

AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation

Bing Li1, Yuanlue Zhu2, Yitong Wang2, Chia-Wen Lin3, Bernard Ghanem1, Linlin Shen4

1Visual Computing Center, KAUST, Thuwal, Saudi Arabia
2ByteDance, Shenzhen, China
3Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan
4Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

Datasets

We build a new dataset called face2anime, which is larger and contains more diverse anime styles (e.g., face poses, drawing styles, colors, hairstyles, eye shapes, strokes, facial contours) than selfie2anime. The face2anime dataset contains 17,796 images in total, where the number of both anime-faces and natural photo-faces is 8,898. The anime-faces are collected from the Danbooru2019 dataset, which contains many anime characters with various anime styles. We employ a pretrained cartoon face detector to select images containing anime-faces. For natural-faces, we randomly select 8,898 female faces from the CelebA-HQ dataset. All images are aligned with facial landmarks and are cropped to size 128 × 128. We separate images from each domain into a training set with 8,000 images and a test set with 898 images.

You can download the face2anime dataset from Google Drive.

Citation

If you find this work useful or use the face2anime dataset, please cite our paper:

@misc{li2021anigan,
      title={AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation}, 
      author={Bing Li and Yuanlue Zhu and Yitong Wang and Chia-Wen Lin and Bernard Ghanem and Linlin Shen},
      year={2021},
      eprint={2102.12593},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

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Comments
  • Contact Info

    Contact Info

    Hello Bing,

    Thanks for the amazing work! I'm trying to reproduce the work and my implementation generates some weird outputs, could you provide the email address that I can reach out to you? Thank you very much!

    opened by Gutianpei 5
  • Code implementation

    Code implementation

    Hi, I've tried to implement according to your paper but no success. Would you please have a loot at my codes (https://github.com/jis478/AniGAN) and give me some directions on improving the results?

    opened by jis478 2
  • Question regarding the encoders

    Question regarding the encoders

    Hi, thanks for the awesome paper. I have some questions regarding the networks in the paper.

    Is there only a single generator covering both domain A and B? How is it supposed to generate both domains' features? (For discriminator it has two branches so it makes sense.)

    Thanks,

    opened by jis478 1
  • Loss function

    Loss function

    Dear author: I have some questions about the loss function:

    1. Is the feature matching loss calculated using pairs (x, G(x, x)) and (y, G(y, y)), where x, y stands for content image and style image, respectively?
    2. Did you merely use G(x, x) and x to calculate the reconstruction loss? Why didn't you calculate another reconstruction loss between G(y, y) and y?

    Looking forward to your reply. Thanks a lot!

    opened by silence-tang 0
Owner
Bing Li
Bing Li
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