Spatially-invariant Style-codes Controlled Makeup Transfer in python
SCGAN Implementation of CVPR 2021 paper “Spatially-invariant Style-codes Controlled Makeup Transfer” Prepare The pre-trained model is avaiable at https://drive.google.com/file/d/1t1Hbgqqzc_rV5v3gF7HuJ-xiuEVNb8sh/view?usp=sharing. vgg_conv.pth:https://drive.google.com/file/d/1JNrSVZrK4TfC7pFG-r7AOmGvBXF2VFOt/view?usp=sharing Put the G.pth and VGG weights in “./checkpoints” and “./” respectively. Environments:python=3.8, pytorch=1.6.0, Ubuntu=20.04.1 LTS Train Put the train-list of makeup images in “./MT-Dataset/makeup.txt” and the train-list of non-makeup images in “./MT-Dataset/non-makeup.txt” Use the “./scripts/handle_parsing.py” to convert the origin MT-Dataset’s seg labels Use python sc.py –phase train to train Test 1.Global Makeup Transfer python sc.py –phase test 2.Part-specific Makeup Transfer […]
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