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A Generative Sketch Model for Human Hair Analysis and Synthesis
July 2006 (vol. 28 no. 7)
pp. 1025-1040
ASCII Text
x 
 
Hong Chen, Song-Chun Zhu, "A Generative Sketch Model for Human Hair Analysis and Synthesis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 7, pp. 1025-1040, July, 2006.
 
 
BibTex
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@article{ 10.1109/TPAMI.2006.131,
author = {Hong Chen and Song-Chun Zhu},
title = {A Generative Sketch Model for Human Hair Analysis and Synthesis},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {28},
number = {7},
issn = {0162-8828},
year = {2006},
pages = {1025-1040},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.131},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - A Generative Sketch Model for Human Hair Analysis and Synthesis
IS - 7
SN - 0162-8828
SP1025
EP1040
EPD - 1025-1040
A1 - Hong Chen,
A1 - Song-Chun Zhu,
PY - 2006
KW - Hair modeling
KW - hair analysis and synthesis
KW - flow patterns
KW - generative models
KW - orientation field
KW - texture
KW - nonphotorealistic rendering.
VL - 28
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
In this paper, we present a generative sketch model for human hair analysis and synthesis. We treat hair images as 2D piecewise smooth vector (flow) fields and, thus, our representation is view-based in contrast to the physically-based 3D hair models in graphics. The generative model has three levels. The bottom level is the high-frequency band of the hair image. The middle level is a piecewise smooth vector field for the hair orientation, gradient strength, and growth directions. The top level is an attribute sketch graph for representing the discontinuities in the vector field. A sketch graph typically has a number of sketch curves which are divided into 11 types of directed primitives. Each primitive is a small window (say 5\times 7 pixels) where the orientations and growth directions are defined in parametric forms, for example, hair boundaries, occluding lines between hair strands, dividing lines on top of the hair, etc. In addition to the three level representation, we model the shading effects, i.e., the low-frequency band of the hair image, by a linear superposition of some Gaussian image bases and we encode the hair color by a color map. The inference algorithm is divided into two stages: 1) We compute the undirected orientation field and sketch graph from an input image and 2) we compute the hair growth direction for the sketch curves and the orientation field using a Swendsen-Wang cut algorithm. Both steps maximize a joint Bayesian posterior probability. The generative model provides a straightforward way for synthesizing realistic hair images and stylistic drawings (rendering) from a sketch graph and a few Gaussian bases. The latter can be either inferred from a real hair image or input (edited) manually using a simple sketching interface. We test our algorithm on a large data set of hair images with diverse hair styles. Analysis, synthesis, and rendering results are reported in the experiments.

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Index Terms:
Hair modeling, hair analysis and synthesis, flow patterns, generative models, orientation field, texture, nonphotorealistic rendering.
Citation:
Hong Chen, Song-Chun Zhu, "A Generative Sketch Model for Human Hair Analysis and Synthesis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 7, pp. 1025-1040, July 2006, doi:10.1109/TPAMI.2006.131
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