Adversarial Colorization Of Icons Based On Structure And Color Conditions

Arxiv: https://arxiv.org/abs/1910.05253

Picture: We compared our system to the current state-of-the-art techniques. From top to bottom rows are the contour images, the icons generated by CycleGAN [47], MUNIT [17], CImg2Img [27], iGAN [46], Anime [44], Comi [8], our system, and the icons referenced by Anime and our system.

Code for reproducing the experiments in the paper:

Tsai-Ho Sun, Chien-Hsun Lai, Sai-Keung Wong, and Yu-Shuen Wang. 2019. Adversarial Colorization Of Icons Based On Structure And Color Conditions. In Proceedings of the 27th ACM International Conference on Multimedia (MM’19), October 21–25, 2019, Nice, France. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3343031.3351041

Abstract: We present a system to help designers create icons that are widely used in banners, signboards, billboards, homepages, and mobile apps. Designers are tasked with drawing contours, whereas our sys- tem colorizes contours in different styles. This goal is achieved by training a dual conditional generative adversarial network (GAN) on our collected icon dataset. One condition requires the generated image and the drawn contour to possess a similar contour, while the other anticipates the image and the referenced icon to be similar in color style. Accordingly, the generator takes a contour image and a man-made icon image to colorize the contour, and then the discriminators determine whether the result fulfills the two condi- tions. The trained network is able to colorize icons demanded by designers and greatly reduces their workload. For the evaluation, we compared our dual conditional GAN to several state-of-the-art techniques. Experiment results demonstrate that our network is over the previous networks. Finally, we will provide the source code, icon dataset, and trained network for public use.

Prerequisites

• pytorch
• torchvision
• tensorboardX

Directory layout

.
├── preprocessed_data
│   ├── contour
│   ├── img
│   ├── labels.pt
├── build_dataset.py
├── dataset.py
├── dump_dataset.py
├── network.py
├── test.py
└── README.md

Usage

python train.py --dataset /path/to/dataset

Dataset

Raw data: https://drive.google.com/open?id=1qtfA_fOCVzU8bigJut1LYKOOlTI20f9d

Preprocessed data: https://drive.google.com/file/d/1B8GjpBPpIxpAEy7cf8xpmA_ESy3CQXLh/view?usp=sharing

GUI

Download: https://drive.google.com/file/d/1ckhFl6Pmx6ltnYd9-geeFQU1z5F3uW-L/view?usp=sharing
The above link include the icon dataset and pretrain weights.

Click the image for the full video demo.