Example-based hair geometry synthesis

Wang, Lvdi; Yu, Yizhou; Zhou, Kun; Guo, Baining

doi:10.1145/1531326.1531362

Cited by 37 publications

(6 citation statements)

References 41 publications

(37 reference statements)

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“…In contrast to prior approaches who start growing at the scalp [Paris et al 2004;Wei et al 2005;Paris et al 2008;Wang et al 2009] filaments start to grow in the most reliable regions of the boundary -measured by rv-and follow the orientation vector field as long as consistent orientation and temperature information is available. Subsequently the shape of the filaments is refined to improve its appearance and finally hair segments are joined at loose ends.…”

Section: Hair Strands Generationmentioning

confidence: 91%

Lighting hair from the inside

Herrera

Zinke²,

Weber

2012

ACM Trans. Graph.

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Generating plausible hairstyles is a very challenging problem. Despite recent efforts no definite solution was presented so far. Many of the current limitations are related to the optical complexity of hair. In this paper we present a technique for hair reconstruction based on thermal imaging. By using this technique several issues of conventional image-based techniques, such as shadowing and anisotropy in reflectance, can be avoided. Moreover, hair-skin segmentation becomes a trivial problem, and no special care about lighting has to be taken, as the hair is "lit from inside" with the head as light source. The capture process is fast and requires a single hand-held device only. The potential of the proposed method is demonstrated by several challenging examples.

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Section: Hair Strands Generationmentioning

confidence: 91%

Lighting hair from the inside

Herrera

Zinke²,

Weber

2012

ACM Trans. Graph.

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“…2, compared with the orientation-based strand tracing method [17], we can obtain more uniformly distributed and less abnormal strand segments. To make strands grow from scalp to tip, we correct the reversed strands using the method similar to [23]. Since strands in ST are uniformly distributed and less abnormal, we utilize them to generate the hybrid orientation field in Sect.…”

Section: Algorithm 1 Tracing One 3d Strand Segmentmentioning

confidence: 99%

Realistic hair modeling from a hybrid orientation field

Bao

2016

Vis Comput

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Image-based hair modeling methods enable artists to produce abundant 3D hair models. However, the reconstructed hair models could not preserve the structural details, such as uniformly distributed hair roots, interior strands growing in line with real distribution and exterior strands similar to images. In this paper, we propose a novel approach to construct a realistic 3D hair model from a hybrid orientation field. Our hybrid orientation field is generated from four fields. The first field makes the surface structure of a hairstyle be similar to the input images as much as possible. The second field makes the hair roots and interior hair strands be consistent with actual distribution. The tracing hair strands can be confined to the hair volume according to the third field. And the fourth field makes the growing direction of one point at a strand be compatible with its predecessor. To generate these fields, we construct high-confidence 3D strand segments from the orientation field of point cloud and 2D traced strands. Hair strands automatically grow from uniformly distributed hair roots according to the hybrid orientation field. We use energy minimization strategy to optimize the entire 3D hair model. We demonstrate that our approach can preserve structural details of 3D hair models.Keywords Image-based hair modeling · Hybrid orientation field · Fractional anisotropy · Preserving structural details · Tracing hair strand Electronic supplementary material The online version of this article (

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“…Yacoob and Davis [3] developed computational models for measuring hair appearance using a multidimensional representation for human identification and hairstyle retrieval. An example-based hair geometry synthesis appeared in [4]. For hair shape modeling, Wang et al [5] presented a part-based model that was robust to both hair shape and variations in the environment.…”

Section: A Related Workmentioning

confidence: 99%

An Unsupervised Hair Segmentation and Counting System in Microscopy Images

Shih

2015

IEEE Sensors J.

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This paper focuses on the development of medical software for clinical applications using advanced image processing algorithms. Three critical issues of hair segmentation and counting are addressed in this paper. First, the removal of any bright spots due to oil or moisture, which generate circular patterns in the middle of the hair and significantly affect the accuracy of determining the line. Second, two contacting or overlapping hairs are recognized and counted as a single hair. To solve this problem, we proposed a hair-bundling algorithm to calculate any concealed hairs. Finally, hairs may be wavy or curly, making the conventional Hough-based line detection algorithm unsuitable, since it suffers from parameter selections, such as the minimum length of line segment, and distance between line segments. Our proposed hair counting algorithm is substantially more accurate than the Hough-based one, and robust to curls, oily scalp, noise-corruption, and overlapping hairs, under various white balance.Index Terms-Hair counting, scalp diagnosis, hair care diagnosis, hair follicle diagnosis, line segment detection.

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Example-based hair geometry synthesis

Cited by 37 publications

References 41 publications

Lighting hair from the inside

Lighting hair from the inside

Realistic hair modeling from a hybrid orientation field

An Unsupervised Hair Segmentation and Counting System in Microscopy Images

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