SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
Abstract
We present a novel image editing system that generates images as the user provides free-form masks, sketches and color as inputs. Our system consists of an end-to-end trainable convolutional network. In contrast to the existing methods, our system utilizes entirely free-form user input in terms of color and shape. This allows the system to respond to the user's sketch and color inputs, using them as guidelines to generate an image. In this work, we trained the network with an additional style loss, which made it possible to generate realistic results despite large portions of the image being removed. Our proposed network architecture SC-FEGAN is well suited for generating high-quality synthetic images using intuitive user inputs.
Cite
Text
Jo and Park. "SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.Markdown
[Jo and Park. "SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.](https://mlanthology.org/iccv/2019/jo2019iccv-scfegan/)BibTeX
@inproceedings{jo2019iccv-scfegan,
title = {{SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color}},
author = {Jo, Youngjoo and Park, Jongyoul},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
year = {2019},
url = {https://mlanthology.org/iccv/2019/jo2019iccv-scfegan/}
}