Official code for CVPR 2020 paper 'Towards Photo-Realistic Virtual Try-On by Adaptively Generating↔Preserving Image Content'. We rearrange the VITON dataset for easy access.
[Dataset Partition Label] [Sample Try-on Video] [Checkpoints]
[Dataset_Test] [Dataset_Train]
python test.pyDataset Partition We present a criterion to introduce the difficulty of try-on for a certain reference image.
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,
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.
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
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.
The use of this software is RESTRICTED to non-commercial research and educational purposes.
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}
}
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.