[ECCV 2020] "Deep Plastic Surgery: Robust and Controllable Image Editing with Human-Drawn Sketches"
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(a) controllable face synthesis | (b) controllable face editing | ||||
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(c) adjusting refinement level l | |||||
Our framework allows users to (a) synthesize and (b) edit photos based on hand-drawn sketches. (c) Our model works robustly on various sketches by setting refinement level l adaptive to the quality of the input sketches, i.e., higher l for poorer sketches, thus tolerating the drawing errors and achieving the controllability on sketch faithfulness. Note that our model requires no real sketches for training. |
This is a pytorch implementation of the paper.
Shuai Yang, Zhangyang Wang, Jiaying Liu and Zongming Guo. Deep Plastic Surgery: Robust and Controllable Image Editing with Human-Drawn Sketches, accepted by European Conference on Computer Vision (ECCV), 2020.
[Project] | [Paper] | [Human-Drawn Facial Sketches]
Please consider citing our paper if you find the software useful for your work.
git clone https://github.com/TAMU-VITA/DeepPS.git
cd DeepPS/src
../save/
../output/
python test.py --l 1.0
python test.py
../output/
python test.py --model_task EDT --input_name ../data/EDT/4.png \
--load_F_name ../save/ECCV-EDT-celebaHQ-F256.ckpt --model_name ECCV-EDT-celebaHQ
--help
to view more testing optionspython test.py --help
../save/
../data/dataset/train/
in form of (I,S):
python train.py --save_model_name PSGAN-SYN
Saved model can be found at ../save/
../data/dataset64/train/
(for example, provided by ContextualGAN)../save/ECCV-SYN-celeba-F64.ckpt
python train.py --train_path ../data/dataset64/ \
--max_dilate 9 --max_level 1 --use_F_level 1 \
--load_F_name ../save/ECCV-SYN-celeba-F64.ckpt --img_size 64 \
--save_model_name PSGAN-SYN-64 --AtoB
python train.py --model_task EDT \
--load_F_name ../save/ECCV-EDT-celebaHQ-F256.ckpt --save_model_name PSGAN-EDT
Saved model can be found at ../save/
--help
to view more training optionspython train.py --help
Shuai Yang