https://github.com/minar09/acgpn
"Towards Photo-Realistic Virtual Try-On by Adaptively Generating↔Preserving Image Content"，CVPR 2020. (Modified from original with fixes for inference)
https://github.com/minar09/acgpn
2020 acgpn cvpr virtual-try-on virtual-tryon viton vton
Last synced: 6 months ago
JSON representation
"Towards Photo-Realistic Virtual Try-On by Adaptively Generating↔Preserving Image Content"，CVPR 2020. (Modified from original with fixes for inference)
- Host: GitHub
- URL: https://github.com/minar09/acgpn
- Owner: minar09
- Created: 2020-09-18T08:48:50.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2023-12-22T05:03:41.000Z (almost 2 years ago)
- Last Synced: 2025-03-25T06:51:15.127Z (7 months ago)
- Topics: 2020, acgpn, cvpr, virtual-try-on, virtual-tryon, viton, vton
- Language: Python
- Homepage: https://github.com/switchablenorms/DeepFashion_Try_On
- Size: 222 KB
- Stars: 80
- Watchers: 8
- Forks: 72
- Open Issues: 17
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Disclaimer
This is just a slightly modified repository of [DeepFashion_Try_On (ACGPN)](https://github.com/switchablenorms/DeepFashion_Try_On) 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]](https://drive.google.com/open?id=1Jt9DykVUmUo5dzzwyi4C_1wmWgVYsFDl) [[Sample Try-on Video]](https://www.youtube.com/watch?v=BbKBSfDBcxI) [[Checkpoints]](https://drive.google.com/file/d/1UWT6esQIU_d4tUm8cjxDKMhB8joQbrFx/view?usp=sharing)
[[Dataset_Test]](https://drive.google.com/file/d/1tE7hcVFm8Td8kRh5iYRBSDFdvZIkbUIR/view?usp=sharing) [[Dataset_Train]](https://drive.google.com/file/d/1lHNujZIq6KVeGOOdwnOXVCSR5E7Kv6xv/view?usp=sharing)
[[Paper]](https://arxiv.org/abs/2003.05863)
## 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
```bash
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](https://github.com/xthan/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.