Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142653
Title: Alive caricature from 2D to 3D
Authors: Wu, Qianyi
Zhang, Juyong
Lai, Yu-Kun
Zheng, Jianmin
Cai, Jianfei
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Wu, Q., Zhang, J., Lai, Y.-K., Zheng, J., & Cai, J. (2018). Alive caricature from 2D to 3D. Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7336-7345. doi:10.1109/CVPR.2018.00766
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.
URI: https://hdl.handle.net/10356/142653
ISBN: 978-1-5386-6421-6
DOI: 10.1109/CVPR.2018.00766
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/CVPR.2018.00766.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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