Official Pytorch implementation for Pose with Style: Detail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN. Please contact Badour AlBahar (badour@vt.edu) if you have any questions.
conda create -n posewithstyle python=3.6
conda activate posewithstyle
conda install pytorch==1.9.0 torchvision==0.10.0 torchaudio==0.9.0 cudatoolkit=10.2 -c pytorch
pip install -r requirements.txt
Intall openCV using conda install -c conda-forge opencv or pip install opencv-python.
If you would like to use wandb, install it using pip install wandb.
You can download the pretrained model here, and the pretrained coordinate completion model here.
Note: we also provide the pretrained model trained on StylePoseGAN [Sarkar et al. 2021] DeepFashion train/test split here. We also provide this split's pretrained coordinate completion model here.
Download the UV space - 2D look up map and save it in util folder.
We provide sample data in data directory. The output will be saved in data/output directory.
python inference.py --input_path ./data --CCM_pretrained_model path/to/CCM_epoch50.pt --pretrained_model path/to/posewithstyle.pt
To repose your own images you need to put the input image (input_name+'.png'), dense pose (input_name+'_iuv.png'), and silhouette (input_name+'_sil.png'), as well as the target dense pose (target_name+'_iuv.png') in data directory.
python inference.py --input_path ./data --input_name fashionWOMENDressesid0000262902_3back --target_name fashionWOMENDressesid0000262902_1front --CCM_pretrained_model path/to/CCM_epoch50.pt --pretrained_model path/to/posewithstyle.pt
Download the UV space - 2D look up map and the UV space body part segmentation. Save both in util folder.
The UV space body part segmentation will provide a generic segmentation of the human body. Alternatively, you can specify your own mask of the region you want to transfer.
We provide sample data in data directory. The output will be saved in data/output directory.
python garment_transfer.py --input_path ./data --CCM_pretrained_model path/to/CCM_epoch50.pt --pretrained_model path/to/posewithstyle.pt --part upper_body
To use your own images you need to put the input image (input_name+'.png'), dense pose (input_name+'_iuv.png'), and silhouette (input_name+'_sil.png'), as well as the garment source target image (target_name+'.png'), dense pose (target_name+'_iuv.png'), and silhouette (target_name+'_sil.png') in data directory. You can specify the part to be transferred using --part as upper_body, lower_body, or face. The output as well as the part transferred (shown in red) will be saved in data/output directory.
python garment_transfer.py --input_path ./data --input_name fashionWOMENSkirtsid0000177102_1front --target_name fashionWOMENBlouses_Shirtsid0000635004_1front --CCM_pretrained_model path/to/CCM_epoch50.pt --pretrained_model path/to/posewithstyle.pt --part upper_body