Wu, Qianyi, Zhang, Juyong, Lai, Yukun, Zheng, Jianmin and Cai, Jianfei
2018.
Alive caricature from 2D to 3D.
Presented at: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),
Lake Salt City, USA,
18-22 Jun 2018.
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Abstract
Caricature is an art form that expresses subjects in abstract,
simple and exaggerated views. While many caricatures
are 2D images, this paper presents an algorithm
for creating expressive 3D caricatures from 2D caricature
images with minimum user interaction. The key idea
of our approach is to introduce an intrinsic deformation
representation that has the capability of extrapolation, enabling
us to create a deformation space from standard face
datasets, which maintains face constraints and meanwhile
is sufficiently large for producing exaggerated face models.
Built upon the proposed deformation representation,
an optimization model is formulated to find the 3D caricature
that captures the style of the 2D caricature image automatically.
The experiments show that our approach has
better capability in expressing caricatures than those fitting
approaches directly using classical parametric face models
such as 3DMM and FaceWareHouse. Moreover, our approach
is based on standard face datasets and avoids constructing
complicated 3D caricature training sets, which
provides great flexibility in real applications.
Item Type: |
Conference or Workshop Item
(Paper)
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Status: |
In Press |
Schools: |
Computer Science & Informatics |
Date of First Compliant Deposit: |
29 March 2018 |
Date of Acceptance: |
19 February 2018 |
Last Modified: |
02 Aug 2019 14:01 |
URI: |
http://orca.cf.ac.uk/id/eprint/110342 |
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