Facial-Sketch-to-Colored-Images-Using-GANs

Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Contributions
4. License
5. Acknowledgments

About The Project

In this project, we tackle the problem of generating color photorealistic images of human faces from corresponding hand-drawn sketches. We aggregate and align datasets CUHK and FS2K of facial sketches and corresponding real facial photos for training and evaluation. For our baseline we tried to follow 4 papers, sketch2face [Julia Gong et al.], pix2pix [Philip Isola et al.], and Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks [Jun-Yan Zhu, Alexei A. Efros et al.]
For more details, please see Report

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Built With

• PyTorch 1.11
• NumPy
• OpenCV

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Getting Started

Prerequisites

• PyTorch

  conda install -c pytorch pytorch

• NumPy

  conda install numpy

• OpenCV

  conda install -c conda-forge opencv

Installation

1. Clone the repo

   git clone https://github.com/kanthprashant/Facial-Sketch-to-Colored-Images.git

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Contributors

1. Parth Goel
2. Prashant Kanth
3. Rishika Bhanushali

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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Acknowledgments

• Philip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros: Image-to-Image Translation with Conditional Adversarial Networks
• Julia Gong, Matthew Mistele: sketch2face: Conditional Generative Adversarial Networks for Transforming Face Sketches into Photorealistic Images.
• Shu-Yu Chen, Wanchao Su, Lin Gao, Shihong Xia, Hongbo Fu: DeepFaceDrawing: Deep Generation of Face Images from Sketches.
• Jun-Yan Zhu, Taesung Park, Philip Isola, Alexei A. Efros: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks.
• Udemy: Computer Vision A-Z, Practical Deep Learning with Pytorch

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