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
Y1 - 2014
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
T2 - 13th European Conference on Computer Vision, ECCV 2014
Y2 - 6 September 2014 through 12 September 2014
ER -