SC-FEGAN: Face editing generative adversarial network with user's sketch and color

Youngjoo Jo; Jongyoul Park

doi:10.1109/ICCV.2019.00183

SC-FEGAN: Face editing generative adversarial network with user's sketch and color

Youngjoo Jo, Jongyoul Park

Dept. of Applied Artificial Intelligence

Electronics and Telecommunications Research Institute

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

268 Scopus citations

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.

Original language	English
Title of host publication	Proceedings - 2019 International Conference on Computer Vision, ICCV 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1745-1753
Number of pages	9
ISBN (Electronic)	9781728148038
DOIs	https://doi.org/10.1109/ICCV.2019.00183
State	Published - Oct 2019
Event	17th IEEE/CVF International Conference on Computer Vision, ICCV 2019 - Seoul, Korea, Republic of Duration: 27 Oct 2019 → 2 Nov 2019

Publication series

Name	Proceedings of the IEEE International Conference on Computer Vision
ISSN (Print)	1550-5499

Conference

Conference	17th IEEE/CVF International Conference on Computer Vision, ICCV 2019
Country/Territory	Korea, Republic of
City	Seoul
Period	27/10/19 → 2/11/19

Access to Document

10.1109/ICCV.2019.00183

Cite this

Jo, Y., & Park, J. (2019). SC-FEGAN: Face editing generative adversarial network with user's sketch and color. In Proceedings - 2019 International Conference on Computer Vision, ICCV 2019 (pp. 1745-1753). Article 9010058 (Proceedings of the IEEE International Conference on Computer Vision). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCV.2019.00183

@inproceedings{26f420763dff4275b4a10d29e901e459,

title = "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.",

author = "Youngjoo Jo and Jongyoul Park",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 17th IEEE/CVF International Conference on Computer Vision, ICCV 2019 ; Conference date: 27-10-2019 Through 02-11-2019",

year = "2019",

month = oct,

doi = "10.1109/ICCV.2019.00183",

language = "English",

series = "Proceedings of the IEEE International Conference on Computer Vision",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1745--1753",

booktitle = "Proceedings - 2019 International Conference on Computer Vision, ICCV 2019",

}

Jo, Y & Park, J 2019, SC-FEGAN: Face editing generative adversarial network with user's sketch and color. in Proceedings - 2019 International Conference on Computer Vision, ICCV 2019., 9010058, Proceedings of the IEEE International Conference on Computer Vision, Institute of Electrical and Electronics Engineers Inc., pp. 1745-1753, 17th IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea, Republic of, 27/10/19. https://doi.org/10.1109/ICCV.2019.00183

SC-FEGAN: Face editing generative adversarial network with user's sketch and color. / Jo, Youngjoo; Park, Jongyoul.
Proceedings - 2019 International Conference on Computer Vision, ICCV 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1745-1753 9010058 (Proceedings of the IEEE International Conference on Computer Vision).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - SC-FEGAN

T2 - 17th IEEE/CVF International Conference on Computer Vision, ICCV 2019

AU - Jo, Youngjoo

AU - Park, Jongyoul

PY - 2019/10

Y1 - 2019/10

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85081890873&partnerID=8YFLogxK

U2 - 10.1109/ICCV.2019.00183

DO - 10.1109/ICCV.2019.00183

M3 - Conference contribution

AN - SCOPUS:85081890873

T3 - Proceedings of the IEEE International Conference on Computer Vision

SP - 1745

EP - 1753

BT - Proceedings - 2019 International Conference on Computer Vision, ICCV 2019

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 27 October 2019 through 2 November 2019

ER -

SC-FEGAN: Face editing generative adversarial network with user's sketch and color

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this