Disclaimer

This is just a slightly modified repository of DeepFashion_Try_On (ACGPN) for inference and visualization. Please refer to the original repository for details.

Towards Photo-Realistic Virtual Try-On by Adaptively Generating↔Preserving Image Content, CVPR'20.

Copy of the Official code for CVPR 2020 paper 'Towards Photo-Realistic Virtual Try-On by Adaptively Generating↔Preserving Image Content'. We rearranged the VITON dataset for easy access.

[Dataset Partition Label] [Sample Try-on Video] [Checkpoints]

[Dataset_Test] [Dataset_Train]

[Paper]

Inference

1. Download the test dataset and unzip
2. Download the checkpoints and unzip
3. Then run - python test.py

Dataset Partition We present a criterion to introduce the difficulty of try-on for a certain reference image.

The specific key points we choose to evaluate the try-on difficulty

We use the pose map to calculate the difficulty level of try-on. The key motivation behind this is the more complex the occlusions and layouts are in the clothing area, the harder it will be. And the formula is given,

The formula to compute the difficulty of try-onreference image

where t is a certain key point, Mp' is the set of key point we take into consideration, and N is the size of the set.

Segmentation Label

0 -> Background
1 -> Hair
4 -> Upclothes
5 -> Left-shoe 
6 -> Right-shoe
7 -> Noise
8 -> Pants
9 -> Left_leg
10 -> Right_leg
11 -> Left_arm
12 -> Face
13 -> Right_arm

Sample images from different difficulty level

Sample Try-on Results

Training Details

For better inference performance, model G and G2 should be trained with 200 epoches, while model G1 and U net should be trained with 20 epoches.

License

The use of this software is RESTRICTED to non-commercial research and educational purposes.

Citation

If you use our code or models in your research, please cite with:

@InProceedings{Yang_2020_CVPR,
author = {Yang, Han and Zhang, Ruimao and Guo, Xiaobao and Liu, Wei and Zuo, Wangmeng and Luo, Ping},
title = {Towards Photo-Realistic Virtual Try-On by Adaptively Generating-Preserving Image Content},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}

Dataset

VITON Dataset This dataset is presented in VITON, containing 19,000 image pairs, each of which includes a front-view woman image and a top clothing image. After removing the invalid image pairs, it yields 16,253 pairs, further splitting into a training set of 14,221 paris and a testing set of 2,032 pairs.