Jen-Vu / Deep-Virtual-Try-On

Deep virtual try on implemented on Flask RESTful API and Android

Worlds first API for Deep Virtual Try On and android implementation

Demo

Google colab link: https://colab.research.google.com/drive/1rlk-nOgIJa14UDNQLJyRxh3McoRLo3b0?usp=sharing

API

Android Application

Results

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 removebg api or holistically-nested-edge-detection 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

Reference

• https://github.com/JDAI-CV/Down-to-the-Last-Detail-Virtual-Try-on-with-Detail-Carving
• https://github.com/CMU-Perceptual-Computing-Lab/openpose
• https://www.pyimagesearch.com/2019/03/04/holistically-nested-edge-detection-with-opencv-and-deep-learning/
• https://www.remove.bg/