This is the official pytorch code for SSAT: A Symmetric Semantic-Aware Transformer Network for Makeup Transfer and Removal, which has been accepted by AAAI-2022. Note that only trained models and test code are provided in pytorch code.
Our latest paper was accepted by CVPR2024 and we will make the code publicly available at this URL https://github.com/Snowfallingplum/CSD-MT as soon as possible.
We have provided the complete training code for the MindSpore version of the SSAT model.
We have provided test samples and trained models, you only need to run the "test.py" file and the results will be in "./results" folder .
- Prepare face parsing. Face parsing is used in this code. In our experiment, face parsing is generated by https://github.com/zllrunning/face-parsing.PyTorch.
- Put the results of face parsing in the .\test\seg1\makeup and \test\seg1\non-makeup
- python test.py.
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