jackzhousz / styleanime

Parsing-Conditioned Anime Translation: A New Dataset and Method(ACM TOG)

paper

Anime is an abstract art form that is substantially different from the human portrait, leading to a challenging misaligned image translation problem that is beyond the capability of existing methods. This can be boiled down to a highly ambiguous unconstrained translation between two domains. To this end, we design a new anime translation framework by deriving the prior knowledge of a pre-trained StyleGAN model. We introduce disentangled encoders to separately embed structure and appearance information into the same latent code, governed by four tailored losses. Moreover, we develop a FaceBank aggregation method that leverages the generated data of the StyleGAN, anchoring the prediction to, produce in-domain animes. To empower our model and promote the research of anime translation, we propose the first anime portrait parsing dataset, Danbooru-Parsing, containing 4,921 densely labeled images across 17 classes. This dataset connects the face semantics with appearances, enabling our new constrained translation setting. We further show the editability of our results, and extend our method to manga images, by generating the first manga parsing pseudo data. Extensive experiments demonstrate the values of our new dataset and method, resulting in the first feasible solution on anime translation.

Description

This is the official implementation of our paper "Parsing-Conditioned Anime Translation: A New Dataset and Method"(ACM TOG).

Danbooru-Parsing Dataset

We train anime parsing model with face-parsing, and the well trained model can be downloaded here.

Label List
0:'background'	1:'skin'	2:'l_brow'	3:'r_brow'
4:'l_eye'	5:'r_eye'	6:'eye_g'	7:'l_ear'
8:'r_ear'	9:'ear_r'	10:'nose'	11:'mouth'
12:'u_lip'	13:'l_lip'	14:'neck'	15:'neck_l'
16:'cloth'	17:'hair'	18:'hat'

Download labeled anime dataset: Danbooru-Parsing Dataset

Pretrained Models

Please download the pre-trained models from the following links.

Path	Description
IR-SE50 Model	Pretrained IR-SE50 model taken from TreB1eN for use in our ID loss during pSp training.
MTCNN	Weights for MTCNN model taken from TreB1eN for use in ID similarity metric computation. (Unpack the tar.gz to extract the 3 model weights.)
CurricularFace Backbone	Pretrained CurricularFace model taken from HuangYG123 for use in ID similarity metric computation.
Anime StyleGAN2 Model	Finetuned model of StyleGAN2 on our anime dataset with StyleGAN2, code from rosinality
Average Latent	Average latent of Stylegan2 pretrained model on anime
Bank List	FaceBank Aggregation, stylegan2 anime latent list
StyleAnime	Our pretrained styleAnime model (portrait2anime)

The pretrained models should be saved to the directory pretrained_models.

Preparing Data

Please first download training dataset:
anime
celeba

Then go to configs/paths_config.py and define:

dataset_paths = {
	'anime_train_segmentation': 'path/anime/anime_seg_train',
	'anime_test_segmentation': 'path/anime/anime_seg_test_68',
	'anime_train': 'path/anime/anime_face_train',
	'anime_test': 'path/anime_face_test_68',
    
	'face_train_segmentation': 'path/celeba/celeba_seg_train',
	'face_test_segmentation': 'path/celeba/celeba_seg_test_68',
	'face_train': 'path/celeba/celeba_face_train',
	'face_test': 'path/celeba/celeba_face_test_68',
}
model_paths = {
	'anime_ffhq': 'pretrained_models/stylegan2_anime_pretrained.pt',
	'ir_se50': 'pretrained_models/model_ir_se50.pth',
	'circular_face': 'pretrained_models/CurricularFace_Backbone.pth',
	'mtcnn_pnet': 'pretrained_models/mtcnn/pnet.npy',
	'mtcnn_rnet': 'pretrained_models/mtcnn/rnet.npy',
	'mtcnn_onet': 'pretrained_models/mtcnn/onet.npy',
}

Training

The main training script can be found in scripts/train.py.

python scripts/train.py
--exp_dir=/path/output
--batch_size=1
--val_interval=2500
--save_interval=5000 
--encoder_type=GradualStyleEncoder
--start_from_latent_avg
--learning_rate=0.0001 
--lpips_lambda=2 --l2_lambda=2.5 
--hm_lambda=0.1
--w_norm_lambda=0.005
--w_norm_lambda_1=0.005
--loss_adv_weight=0.1 
--loss_adv_weight_latent=0.1 
--label_nc=19 
--input_nc=19
--test_batch_size=1

Testing

python scripts/inference_latent.py --exp_dir=/path/portrait2anime_results --checkpoint_path=./pretrained_models/best_model.pt --test_batch_size=1

We assume that all pretrained models are downloaded and saved to the directory pretrained_models.

Acknowledgments

This code borrows heavily from pixel2style2pixel

To Do List

• Release the Anime2Portrait code

Citation

If you use this code for your research, please cite our paper Parsing-Conditioned Anime Translation: A New Dataset and Method :

@article{li2023parsing,
  title={Parsing-Conditioned Anime Translation: A New Dataset and Method},
  author={Li, Zhansheng and Xu, Yangyang and Zhao, Nanxuan and Zhou, Yang and Liu, Yongtuo and Lin, Dahua and He, Shengfeng},
  journal={ACM Transactions on Graphics},
  year={2023},
  publisher={ACM New York, NY}
}