# TP-GAN

**Repository Path**: xander23333/TP-GAN

## Basic Information

- **Project Name**: TP-GAN
- **Description**: Official TP-GAN Tensorflow implementation for paper "Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis"
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-09-06
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# TP-GAN

Official TP-GAN Tensorflow implementation for the ICCV17 paper "[Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis](http://openaccess.thecvf.com/content_ICCV_2017/papers/Huang_Beyond_Face_Rotation_ICCV_2017_paper.pdf)" by Huang, Rui and Zhang, Shu and Li, Tianyu and He, Ran.

The goal is to **recover a frontal face image of the same person from a single face image under any poses**.

Here are some examples from the paper.![image](images/ownsynthesis.jpg)

### Testing images

Synthesized  testing images of all poses, corresponding illumination in Setting 2 (and its cropped input) in MultiPIE can be obtained here [Google Drive](https://drive.google.com/file/d/1Kx0sMjFTzLX3-rZ03TAVBAj-gcd9rJrd/view?usp=sharing). 

Synthesized images for other illumination condition and/or training set can be obtained upon request. If you would like to access the original MultiPIE dataset, please contact [MultiPIE](http://www.cs.cmu.edu/afs/cs/project/PIE/MultiPie/Multi-Pie/Home.html).

### Random examples

Here are random examples of 10 testing image pairs for each degree.

15 and 30 degrees: 
<p align="center">
<img src="images/15-rand.png", width="300", border="10">   <img src="images/30-rand.png", width="300", border="10">
</p> 

45 and 60 degrees:
<p align="center">
<img src="images/45-rand.png", width="300", border="10">   <img src="images/60-rand.png", width="304", border="10">
</p> 

75 and 90 degrees:
<p align="center">
<img src="images/75-rand.png", width="300", border="10">   <img src="images/90-rand.png", width="306", border="10">
</p> 

### Note

It was initially written in Tensorflow 0.12.

This is an initial release of code, which may not be fully tested. Refinement, input data example, pre-trained models, and precomputed testing image features will come later.

The input is cropped with the Matlab script `face_db_align_single_custom.m`, which accepts 5 keypoints and outputs a cropped image and transformed keypoints.

Some example cropping outputs is shown in folder `data-example`.

The keypoints can be extracted from off-the-shelf landmark detectors, e.g. 'Zhang et al. Combining Data-driven and Model-driven Methods for Robust Facial Landmark Detection, 2016'. The synthesis performance is similar to using manually labelled keypoints.

We thank Xiang Wu for providing the [face feature network](https://github.com/AlfredXiangWu/face_verification_experiment). We load it as `DeepFace` in the code, the weights are from a custom Light-CNN cafeemodel file.


### Citation and Contact

If you like our work or find our code useful, welcome to cite our paper!

Any suggestion and/or comment would be valuable. Please send an email to Rui at huangrui@cmu.edu or other authors.

      @InProceedings{Huang_2017_ICCV,
      author = {Huang, Rui and Zhang, Shu and Li, Tianyu and He, Ran},
      title = {Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis},
      booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
      month = {Oct},
      year = {2017}
      }

### License

This code is freely available for free non-commercial use, and may be redistributed under the conditions set by the license. Please, see the [license](https://github.com/HRLTY/TP-GAN/blob/master/LICENSE) for further details. For commercial queries, please contact [Rui Huang](http://www.andrew.cmu.edu/user/ruih2/) and [Ran He](http://www.nlpr.ia.ac.cn/english/irds/People/rhe.html).