This is the unofficial implementation of 'SieveNet: A Unified Framework for Robust Image-Based Virtual Try-On'
Paper can be found from here
Dataset download instructions and link of dataset can be found from official repo of CP-VTON and VITON
Put dataset in data
folder
Clone the repo and install requirements through pip install -r requirements.txt
In config.py
set self.datamode='train'
and self.stage='GMM'
then run python train.py
You can observe results while traning in tensorboard as below
In config.py
set self.datamode='Train'
and self.stage='SEG'
then run python train.py
In config.py
set self.datamode='Train'
and self.stage='TOM'
then run python train.py
Please download checkpoint of all three modules from google drive and put them in checkpoints
folder
For testing, in config.py
set self.datamode='test'
For Testing of Coarse-to-Fine Warping module, Conditional Segmentation Mask generation module, and Segmentation Assisted Texture Translation module set self.stage='GMM'
, self.stage='SEG'
, and self.stage='TOM'
respectively.
Here is testing result. For Coarse-to-Fine Warping module,
For Segmentation Assisted Texture Translation module,
- Please download checkpoint of all three modules from google drive and put them in
checkpoints
folder. - Please download caffe-model from here and put the model in
pose
folder. - Generate human parsing from Self-Correction-Human-Parsing repo. Select
LIP
dataset while generating human parsing. - Set input-image's, cloth-image's, and output of human parsing image's path in config file.
- Then run
python inference.py
Output will be saved inoutputs
folder.
Some modules of this implementation is based on this repo
For generating pose keypoints, I have used learnopencv implementation of OpenPose