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Snowfallingplum / SSAT

[AAAI 2022] This is the official pytorch code for SSAT: A Symmetric Semantic-Aware Transformer Network for Makeup Transfer and Removal
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SSAT: A Symmetric Semantic-Aware Transformer Network for Makeup Transfer and Removal

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.

Latest News

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.

Training code

We have provided the complete training code for the MindSpore version of the SSAT model.

Example

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 .

How to test a custom dataset

  1. 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.
  2. Put the results of face parsing in the .\test\seg1\makeup and \test\seg1\non-makeup
  3. python test.py.

Our results

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