Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151525
Title: Vectorization based color transfer for portrait images
Authors: Fu, Qian
He, Ying
Hou, Fei
Zhang, Juyong
Zeng, Anxiang
Liu, Yong-Jin
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Fu, Q., He, Y., Hou, F., Zhang, J., Zeng, A. & Liu, Y. (2019). Vectorization based color transfer for portrait images. Computer Aided Design, 115, 111-121. https://dx.doi.org/10.1016/j.cad.2019.05.005
Project: RG26/17
Journal: Computer Aided Design
Abstract: This paper introduces a method for transferring colors between portrait images. Using a trained neural network to extract facial mask, we vectorize each image with a set of sparse diffusion curves to encode the low-frequency colors, and use the Laplacian of residual colors to represent the high-frequency details. Then we apply optimal mass transport to transfer the boundary colors between the diffusion curves of the source and reference images. Finally, the original or modified Laplacians of colors are added to the transferred diffusion curve image. Unlike the existing methods that either require 3D information or assume the source and reference images have similar poses and dense correspondence, our method is computationally efficient and flexible, which can work for portrait images with large pose and color differences.
URI: https://hdl.handle.net/10356/151525
ISSN: 0010-4485
DOI: 10.1016/j.cad.2019.05.005
Rights: © 2019 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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