Synthesis of exaggerative caricature with inter and intra correlations

Chien Chung Tseng, James Jenn-Jier Lien

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

15 Citations (Scopus)

Abstract

We developed a novel system consisting of two modules, statisticsbased synthesis and non-photorealistic rendering (NPR), to synthesize caricatures of exaggerated facial features and other particular characteristics, such as beards or nevus. The statistics-based synthesis module can exaggerate shapes and positions of facial features based on non-linear exaggerative rates determined automatically. Instead of comparing only the inter relationship between features of different subjects at the existing methods, our synthesis module applies both inter and intra (i.e. comparisons between facial features of the same subject) relationships to make the synthesized exaggerative shape more contrastive. Subsequently, the NPR module generates a line-drawing sketch of original face, and then the sketch is warped to an exaggerative style with synthesized shape points. The experimental results demonstrate that this system can automatically, and effectively, exaggerate facial features, thereby generating corresponding facial caricatures.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings
Pages314-323
Number of pages10
Volume4843 LNCS
EditionPART 1
Publication statusPublished - 2007
Event8th Asian Conference on Computer Vision, ACCV 2007 - Tokyo, Japan
Duration: 2007 Nov 182007 Nov 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume4843 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th Asian Conference on Computer Vision, ACCV 2007
CountryJapan
CityTokyo
Period07-11-1807-11-22

Fingerprint

Non-photorealistic Rendering
Synthesis
Module
Line Drawing
Statistics
Face
Experimental Results
Demonstrate
Relationships

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Tseng, C. C., & Lien, J. J-J. (2007). Synthesis of exaggerative caricature with inter and intra correlations. In Computer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings (PART 1 ed., Vol. 4843 LNCS, pp. 314-323). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4843 LNCS, No. PART 1).
Tseng, Chien Chung ; Lien, James Jenn-Jier. / Synthesis of exaggerative caricature with inter and intra correlations. Computer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings. Vol. 4843 LNCS PART 1. ed. 2007. pp. 314-323 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).
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abstract = "We developed a novel system consisting of two modules, statisticsbased synthesis and non-photorealistic rendering (NPR), to synthesize caricatures of exaggerated facial features and other particular characteristics, such as beards or nevus. The statistics-based synthesis module can exaggerate shapes and positions of facial features based on non-linear exaggerative rates determined automatically. Instead of comparing only the inter relationship between features of different subjects at the existing methods, our synthesis module applies both inter and intra (i.e. comparisons between facial features of the same subject) relationships to make the synthesized exaggerative shape more contrastive. Subsequently, the NPR module generates a line-drawing sketch of original face, and then the sketch is warped to an exaggerative style with synthesized shape points. The experimental results demonstrate that this system can automatically, and effectively, exaggerate facial features, thereby generating corresponding facial caricatures.",
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Tseng, CC & Lien, JJ-J 2007, Synthesis of exaggerative caricature with inter and intra correlations. in Computer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings. PART 1 edn, vol. 4843 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 4843 LNCS, pp. 314-323, 8th Asian Conference on Computer Vision, ACCV 2007, Tokyo, Japan, 07-11-18.

Synthesis of exaggerative caricature with inter and intra correlations. / Tseng, Chien Chung; Lien, James Jenn-Jier.

Computer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings. Vol. 4843 LNCS PART 1. ed. 2007. p. 314-323 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4843 LNCS, No. PART 1).

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

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AB - We developed a novel system consisting of two modules, statisticsbased synthesis and non-photorealistic rendering (NPR), to synthesize caricatures of exaggerated facial features and other particular characteristics, such as beards or nevus. The statistics-based synthesis module can exaggerate shapes and positions of facial features based on non-linear exaggerative rates determined automatically. Instead of comparing only the inter relationship between features of different subjects at the existing methods, our synthesis module applies both inter and intra (i.e. comparisons between facial features of the same subject) relationships to make the synthesized exaggerative shape more contrastive. Subsequently, the NPR module generates a line-drawing sketch of original face, and then the sketch is warped to an exaggerative style with synthesized shape points. The experimental results demonstrate that this system can automatically, and effectively, exaggerate facial features, thereby generating corresponding facial caricatures.

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Tseng CC, Lien JJ-J. Synthesis of exaggerative caricature with inter and intra correlations. In Computer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings. PART 1 ed. Vol. 4843 LNCS. 2007. p. 314-323. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).