ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2018, Vol. 55 ›› Issue (11): 2543-2556.doi: 10.7544/issn1000-1239.2018.20170390

• 图形图像 • 上一篇    下一篇

基于图像的头发建模技术综述

包永堂1,2,齐越2,3   

  1. 1(College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590); 2(State Key Laboratory of Virtual Reality Technology and Systems (Beihang University), Beijing 100083); 3(Qingdao Research Institute of Beihang University, Qingdao, Shandong 266100)
  • 出版日期: 2018-11-01
  • 基金资助: 
    国家自然科学基金项目(61572054);国家重点研发计划项目(2017YFB1002602);青岛市应用基础研究计划基金项目(16-10-1-3-xx)

A Survey on Image-Based Hair Modeling Technique

Bao Yongtang1,2, Qi Yue2,3   

  1. 1(山东科技大学计算机科学与工程学院 山东青岛 266590); 2(虚拟现实技术与系统国家重点实验室(北京航空航天大学) 北京 100083) (北京航空航天大学青岛研究院 山东青岛 266100) (baozi0221@163.com)
  • Online: 2018-11-01

摘要: 头发是人类特征的重要组成部分,是判断一个人的年龄、背景及身份等的重要依据.在虚拟现实和计算机图形学领域,头发建模受到越来越多的研究人员的关注.传统的头发建模是基于物理模拟的,通过设置相关参数构造出不同的头发,计算量很大,建模过程也不直观、难以控制;而基于图像的头发建模方法,具有建模速度快、模型逼真度高等优点,近几年受到研究者重视,开始成为另外一个研究热点.回顾了基于单幅图像的头发建模、基于多幅图像的静态头发建模、基于视频的动态头发建模、头发建模结果的编辑重用等方面的研究进展,并分析总结了各种方法的适用情况及不足,最后提出了基于图像的头发建模技术在未来的发展趋势以及面临的挑战.

关键词: 头发建模, 基于图像的头发建模, 头发编辑重用, 头发仿真, 方向场

Abstract: Hair is a vital component of a person’s identity, and it can provide strong cues about age, background and even personality. More and more researchers focus on hair modeling in the field of computer graphics and virtual reality. Traditional hair modeling method is physically-based simulation by setting different parameters. The computation is expensive, and the construction process is not intuitive and difficult to control. The image-based hair modeling method has the advantages of fast modeling and high fidelity. In recent years, researchers have attached great importance to this method. This paper reviews the development of hair modeling based on single image, static hair modeling based on multiple images, dynamic hair modeling based on captured videos, and the editing and reusing of hair modeling results. It also analyzes and summarizes the application and inadequate of each method. In the first section, it summarizes the single-image based hair modeling method which can be divided into two types of orientation-field-based and data-driven-based hair modeling method. The static hair modeling and dynamic hair capture methods are reviewed in the next two parts. The static hair modeling based on multiple images also can be divided into two types of orientation-field-based and data-driven-based method. In the last section, the editing and reusing of hair modeling results are reviewed. The future development trends and challenges of image-based hair modeling are proposed in the end.

Key words: hair modeling, image-based hair modeling, hair editing and reusing, hair simulation, orientation field

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