Abstract
In this paper, we address the problem of synthesizing continuous variations with the appearance of makeup by taking a linear combination of the examples. Makeup usually shows a vague boundary and does not form a clear shape, which makes this problem unique from the existing image interpolation problems. We approach this problem as an interpolation between semi-transparent image layers and tackle this by presenting new parametrization schemes for the color and for the shape separately in order to achieve an effective interpolation. For the color parametrization, our main idea is based on the observation of the symmetric relation between the color and transparency of the makeup; we provide an optimization framework for extracting a representative palette of colors associated with the transparent values, which enables us to easily set up the color correspondence among the multiple makeup samples. For the shape parametrization, we exploit a polar coordinate system, that creates the in-between shapes effectively, without ghosting artifacts.
Highlights
Digital makeup has become more important in recent years, and a great deal of effort has been devoted to the study of believably transferring a example makeup to a new subject [1,2]
We present a method for modifying the existing makeup examples by taking a linear combination of them. This linear combination of the examples can be more intuitive than conventional image manipulation techniques; this can be especially prevalent for the novice users who do not know what they want exactly, and it expands the possibilities for the makeup suggestion systems to enable continuous variation of the output makeup
We provide an optimization framework for extracting a representative palette of colors associated with the transparent values for each given makeup image layer
Summary
Digital makeup has become more important in recent years, and a great deal of effort has been devoted to the study of believably transferring a example makeup to a new subject [1,2]. This linear combination of the examples can be more intuitive than conventional image manipulation techniques; this can be especially prevalent for the novice users who do not know what they want exactly, and it expands the possibilities for the makeup suggestion systems to enable continuous variation of the output makeup. We approach this problem as an interpolation between semi-transparent images because the conventional digital makeup process ends up with adding a semi-transparent makeup image layer over a given target no-makeup facial image.
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