Implementation of CVPR 2021 paper "Spatially-invariant Style-codes Controlled Makeup Transfer"

Related tags

Deep Learning SCGAN
Overview

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

Global Makeup Transfer

2.Part-specific Makeup Transfer

python sc.py --phase test --partial

Part-specific Makeup Transfer

3.Global Interpolation

python sc.py --phase test --interpolation

Global Interpolation

4.Part-specific Interpolation

python sc.py --phase test --partial --interpolation

Part-specific Interpolation

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Comments
  • how to modify the output picture size?

    how to modify the output picture size?

    Suppose I input a 2048X2048 picture, how to output the result picture of 2048X2048, the code is no matter how many size pictures are input, the output is 256X256 pictures, I tried to modify img_size, the result is an error, how to modify the output picture size?

    opened by seawater668 0
  • Influence of style_dim

    Influence of style_dim

    Style_dim is 192 in the code, and the implementation details are not explained in the article. May I ask the basis for setting style_DIM like this, and what impact will it have if it is changed

    opened by chqaqc 0
  • why need to run handle_parsing before training.

    why need to run handle_parsing before training.

    This script convert the label to anther, and only used when decompose face in Datasetloader. It seems no helpness with training. Any suggestion is appreciated.

    opened by 2h4dl 0
  • my modification to reproduce result

    my modification to reproduce result

    I found there are two problems in this code.

    First, the vgg loss relation is wrong: in file models/SCGAN.py, in line 291, vgg_s should apply to nonmakeup; in line 297, vgg_r should apply to makeup, otherwise the result will be furry like https://github.com/makeuptransfer/SCGAN/issues/10.

    Second, vgg19 module's weight in PartStyleEncoder will be overwritten by code in models/SCGAN.py line 63, so maybe you can load it's weight in line 64.

    After these modification, the result is still not right, and the gradient in PartStyleEncoder and MLP is very small (Hong-Bo tells me).

    Finally I use the pretrained weight provided by the author only in these two modules, and train FaceEncoder and MakeupFuseDecoder, the result is right.

    But it's not ideal, waitting for better guidance.

    opened by hustliujian 11
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