2012
DOI: 10.1145/2185520.2185612
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Single-view hair modeling for portrait manipulation

Abstract: Figure 1: Given a portrait image and a few strokes drawn by the user as input (a), our method generates a strand-based 3D hair model as shown in (b), where a fraction of reconstructed fibers are highlighted. The hair model can be used to convert the input portrait into a pop-up model (c) which can be rendered in a novel view (d). It also enables several interesting applications such as transferring the hairstyle of one subject to another (e). Original images courtesy of Getty Images (a) and Andrew MacPherson (… Show more

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Cited by 88 publications
(68 citation statements)
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References 34 publications
(31 reference statements)
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“…Mostly the users requirement is immediately do facial beautification with the minimum number of operations to avoid endless manipulations. There for it is useful to develop a face image enhancement technique that is effective ,convenient, and flexible.The increasing demand for face image retouching has worked on many methods,such as facial geometric beautification [34],digital facial makeup [29], personal photo enhancement [20], and hair modeling [21].…”
Section: Literature Reviewmentioning
confidence: 99%
“…Mostly the users requirement is immediately do facial beautification with the minimum number of operations to avoid endless manipulations. There for it is useful to develop a face image enhancement technique that is effective ,convenient, and flexible.The increasing demand for face image retouching has worked on many methods,such as facial geometric beautification [34],digital facial makeup [29], personal photo enhancement [20], and hair modeling [21].…”
Section: Literature Reviewmentioning
confidence: 99%
“…Guided by hair simulation, Zhang et al [29] reconstructed smooth hair dynamics with 7 cameras. Using only a single view, Chai et al [4] proposed a method to generate a depth map for convincing view interpolation of different hairstyles. Beeler at al.…”
Section: Hair Capturementioning
confidence: 99%
“…The orientation map is enhanced with 3 passes of iterative refinement to improve the signal-to-noise ratio as in [4]. To further reduce noises in regions with low confidence, we apply the bilateral filtering method in [20] to diffuse the orientations of the high confidence regions.…”
Section: Strand Initializationmentioning
confidence: 99%
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