Real-time exemplar-based face sketch synthesis

Yibing Song, Linchao Bao, Qingxiong Yang, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

62 Citations (Scopus)

Abstract

This paper proposes a simple yet effective face sketch synthesis method. Similar to existing exemplar-based methods, a training dataset containing photo-sketch pairs is required, and a K-NN photo patch search is performed between a test photo and every training exemplar for sketch patch selection. Instead of using the Markov Random Field to optimize global sketch patch selection, this paper formulates face sketch synthesis as an image denoising problem which can be solved efficiently using the proposed method. Real-time performance can be obtained on a state-of-the-art GPU. Meanwhile quantitative evaluations on face sketch recognition and user study demonstrate the effectiveness of the proposed method. In addition, the proposed method can be directly extended to the temporal domain for consistent video sketch synthesis, which is of great importance in digital entertainment.

Original languageEnglish
Title of host publicationComputer Vision, ECCV 2014 - 13th European Conference, Proceedings
PublisherSpringer Verlag
Pages800-813
Number of pages14
EditionPART 6
ISBN (Print)9783319105987
DOIs
Publication statusPublished - 2014 Jan 1
Event13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland
Duration: 2014 Sep 62014 Sep 12

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 6
Volume8694 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th European Conference on Computer Vision, ECCV 2014
CountrySwitzerland
CityZurich
Period14/9/614/9/12

Fingerprint

Face
Synthesis
Real-time
Patch
Image denoising
Face recognition
Quantitative Evaluation
Image Denoising
User Studies
Face Recognition
Random Field
Optimise
Demonstrate
Training
Graphics processing unit

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Song, Y., Bao, L., Yang, Q., & Yang, M. H. (2014). Real-time exemplar-based face sketch synthesis. In Computer Vision, ECCV 2014 - 13th European Conference, Proceedings (PART 6 ed., pp. 800-813). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8694 LNCS, No. PART 6). Springer Verlag. https://doi.org/10.1007/978-3-319-10599-4_51
Song, Yibing ; Bao, Linchao ; Yang, Qingxiong ; Yang, Ming Hsuan. / Real-time exemplar-based face sketch synthesis. Computer Vision, ECCV 2014 - 13th European Conference, Proceedings. PART 6. ed. Springer Verlag, 2014. pp. 800-813 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 6).
@inproceedings{0f29841343be47d482dd0dbaaf9c43cd,
title = "Real-time exemplar-based face sketch synthesis",
abstract = "This paper proposes a simple yet effective face sketch synthesis method. Similar to existing exemplar-based methods, a training dataset containing photo-sketch pairs is required, and a K-NN photo patch search is performed between a test photo and every training exemplar for sketch patch selection. Instead of using the Markov Random Field to optimize global sketch patch selection, this paper formulates face sketch synthesis as an image denoising problem which can be solved efficiently using the proposed method. Real-time performance can be obtained on a state-of-the-art GPU. Meanwhile quantitative evaluations on face sketch recognition and user study demonstrate the effectiveness of the proposed method. In addition, the proposed method can be directly extended to the temporal domain for consistent video sketch synthesis, which is of great importance in digital entertainment.",
author = "Yibing Song and Linchao Bao and Qingxiong Yang and Yang, {Ming Hsuan}",
year = "2014",
month = "1",
day = "1",
doi = "10.1007/978-3-319-10599-4_51",
language = "English",
isbn = "9783319105987",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 6",
pages = "800--813",
booktitle = "Computer Vision, ECCV 2014 - 13th European Conference, Proceedings",
address = "Germany",
edition = "PART 6",

}

Song, Y, Bao, L, Yang, Q & Yang, MH 2014, Real-time exemplar-based face sketch synthesis. in Computer Vision, ECCV 2014 - 13th European Conference, Proceedings. PART 6 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 6, vol. 8694 LNCS, Springer Verlag, pp. 800-813, 13th European Conference on Computer Vision, ECCV 2014, Zurich, Switzerland, 14/9/6. https://doi.org/10.1007/978-3-319-10599-4_51

Real-time exemplar-based face sketch synthesis. / Song, Yibing; Bao, Linchao; Yang, Qingxiong; Yang, Ming Hsuan.

Computer Vision, ECCV 2014 - 13th European Conference, Proceedings. PART 6. ed. Springer Verlag, 2014. p. 800-813 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8694 LNCS, No. PART 6).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Real-time exemplar-based face sketch synthesis

AU - Song, Yibing

AU - Bao, Linchao

AU - Yang, Qingxiong

AU - Yang, Ming Hsuan

PY - 2014/1/1

Y1 - 2014/1/1

N2 - This paper proposes a simple yet effective face sketch synthesis method. Similar to existing exemplar-based methods, a training dataset containing photo-sketch pairs is required, and a K-NN photo patch search is performed between a test photo and every training exemplar for sketch patch selection. Instead of using the Markov Random Field to optimize global sketch patch selection, this paper formulates face sketch synthesis as an image denoising problem which can be solved efficiently using the proposed method. Real-time performance can be obtained on a state-of-the-art GPU. Meanwhile quantitative evaluations on face sketch recognition and user study demonstrate the effectiveness of the proposed method. In addition, the proposed method can be directly extended to the temporal domain for consistent video sketch synthesis, which is of great importance in digital entertainment.

AB - This paper proposes a simple yet effective face sketch synthesis method. Similar to existing exemplar-based methods, a training dataset containing photo-sketch pairs is required, and a K-NN photo patch search is performed between a test photo and every training exemplar for sketch patch selection. Instead of using the Markov Random Field to optimize global sketch patch selection, this paper formulates face sketch synthesis as an image denoising problem which can be solved efficiently using the proposed method. Real-time performance can be obtained on a state-of-the-art GPU. Meanwhile quantitative evaluations on face sketch recognition and user study demonstrate the effectiveness of the proposed method. In addition, the proposed method can be directly extended to the temporal domain for consistent video sketch synthesis, which is of great importance in digital entertainment.

UR - http://www.scopus.com/inward/record.url?scp=84906342317&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84906342317&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-10599-4_51

DO - 10.1007/978-3-319-10599-4_51

M3 - Conference contribution

AN - SCOPUS:84906342317

SN - 9783319105987

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 800

EP - 813

BT - Computer Vision, ECCV 2014 - 13th European Conference, Proceedings

PB - Springer Verlag

ER -

Song Y, Bao L, Yang Q, Yang MH. Real-time exemplar-based face sketch synthesis. In Computer Vision, ECCV 2014 - 13th European Conference, Proceedings. PART 6 ed. Springer Verlag. 2014. p. 800-813. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 6). https://doi.org/10.1007/978-3-319-10599-4_51