3D Hair Synthesis Using Volumetric Variational Autoencoders (bibtex)
by Shunsuke Saito, Liwen Hu, Chongyang Ma, Hikaru Ibayashi, Linjie Luo, Hao Li
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 (Shunsuke Saito, Liwen Hu, Chongyang Ma, Hikaru Ibayashi, Linjie Luo, Hao Li), 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},
}
Powered by bibtexbrowser