Down to the Last Detail: Virtual Try-on with Detail Carving

Code for virtual try-on with high-fidelity details. The code was developed and tested with Pytorch0.4.1.

Getting Started

Installation

• Clone this repo

  git clone https://github.com/AIprogrammer/Detailed-virtual-try-on.git. 
  cd Detailed-virtual-try-on
  

• Download our pretrained models from Google Drive, and put them in "./pretrained_checkpoint".

Demo

• We provide a demo model, as well as some samples in "./dataset/images". Triplets including source image, target pose, target cloth is provided in the "./demo/demo.txt".
• Quick testing and checking results in "./demo/forward/0.jpg" by running

  sh demo.sh
  

Training

Download the dataset

• Download the MPV dataset from Image-based Multi-pose Virtual Try On and put the dataset under "./dataset/images/".
• Select postive perspective images, create dataset split file 'data_pair.txt', and put it under "./dataset/".

Dataset preprocessing

• Pose keypoints. Use the Openpose, and put the keypoints file in "./dataset/pose_coco".
• Semantic parsing. Use the CIHP_PGN, and put the parsing results in "./dataset/parse_cihp".
• Cloth mask. Use the "GrabCut" method for the cloth mask, and put the mask in "./dataset/cloth_mask".

Coarse-to-fine training

• Download the VGG19 pretrained checkpoint

  cd vgg_model/
  wget https://download.pytorch.org/models/vgg19-dcbb9e9d.pth
  

• Set different configuration based on the "config.py". Then run

  sh train.sh
  

Related Publications

If you find this code helpful, please cite our paper: ``` @inproceedings{detail2019, title={Down to the Last Detail: Virtual Try-on with Detail Carving}, author={Wang, Jiahang and Zhang, Wei and Liu, Weizhong and Mei, Tao}, booktitle = {arXiv:1912.06324}, year={2019} }

@inproceedings{cvpr19, title={Unsupervised Person Image Generation with Semantic Parsing Transformation}, author={Song, Sijie and Zhang, Wei and Liu, Jiaying and Mei, Tao}, booktitle = {CVPR}, year={2019} }

@inproceedings{pami20, title={Unpaired Person Image Generation with Semantic Parsing Transformation}, author={Song, Sijie and Zhang, Wei and Liu, Jiaying and Guo, Zongming and Mei, Tao}, booktitle = {IEEE Trans. on PAMI}, year={2020} }