@inproceedings{6666e5651a614b3cbbcabde3fe1c38b2,
title = "Automatic hair modeling from one image",
abstract = "Hair is one of the most critical characteristics of a person in the process of digitizing characters, but at the same time, hair modeling is still a very challenging task due to the diversity of hairstyles and the overlap among hair. We introduce a method to automatically generate 3D hair geometry from a front hair image, which can recover the outline and details of the hair geometry. We designed a encoder-decoder convolutional neural network which takes the 2D orientation field from a hair image as input, and output the characteristic hair geometry. Then we use the characteristic strands to search for eligible hairstyle from Hairstyle-database, and fuse retrieved hair model to get the nal hairstyle. This pipeline can automatically recover hair geometry from a front hair image without any supplementary information. Experimental results show that our approach achieves realistic reconstruction effect from real Internet pictures and self-portraits.",
keywords = "Deep Learning, Hairstyles Database, Orientation Field, Single-view Hair Modeling",
author = "Ligang Cheng and Yongtang Bao and Yue Qi",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 9th International Conference on Virtual Reality and Visualization, ICVRV 2019 ; Conference date: 21-11-2019 Through 22-11-2019",
year = "2019",
month = nov,
doi = "10.1109/ICVRV47840.2019.00026",
language = "英语",
series = "Proceedings - 2019 International Conference on Virtual Reality and Visualization, ICVRV 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "108--112",
editor = "Dangxiao Wang and Cadavid, \{Andres Navarro\} and Yue Liu and Mingliang Xu",
booktitle = "Proceedings - 2019 International Conference on Virtual Reality and Visualization, ICVRV 2019",
address = "美国",
}