by Saito, Shunsuke, Hu, Liwen, Ma, Chongyang, Ibayashi, Hikaru, Luo, Linjie and Li, Hao
Abstract:
Recent advances in single-view 3D hair digitization have made the creation of high-quality CG characters scalable and accessible to end-users, enabling new forms of personalized VR and gaming experiences.
Reference:
3D Hair Synthesis Using Volumetric Variational Autoencoders (Saito, Shunsuke, Hu, Liwen, Ma, Chongyang, Ibayashi, Hikaru, Luo, Linjie and Li, Hao), In SIGGRAPH Asia 2018 Technical Papers, ACM, 2018.
Bibtex Entry:
@inproceedings{Saito:2018:HSU:3272127.3275019,
author = {Saito, Shunsuke and Hu, Liwen and Ma, Chongyang and Ibayashi, Hikaru and Luo, Linjie and Li, Hao},
title = {3D Hair Synthesis Using Volumetric Variational Autoencoders},
booktitle = {SIGGRAPH Asia 2018 Technical Papers},
series = {SIGGRAPH Asia '18},
year = {2018},
abstract ={Recent advances in single-view 3D hair digitization have made the creation of high-quality CG characters scalable and accessible to end-users, enabling new forms of personalized VR and gaming experiences.},
isbn = {978-1-4503-6008-1},
location = {Tokyo, Japan},
pages = {208:1--208:12},
articleno = {208},
numpages = {12},
url = {http://doi.acm.org/10.1145/3272127.3275019},
doi = {10.1145/3272127.3275019},
acmid = {3275019},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {deep generative model, hair synthesis, single-view modeling, volumetric variational autoencoder},
}