Production-level facial performance capture using deep convolutional neural networks (bibtex)

by Laine, Samuli, Karras, Tero, Aila, Timo, Herva, Antti, Saito, Shunsuke, Yu, Ronald, Li, Hao and Lehtinen, Jaakko

Abstract:

We present a real-time deep learning framework for video-based facial performance capture – the dense 3D tracking of an actor's face given a monocular video.

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Reference:

Production-level facial performance capture using deep convolutional neural networks (Laine, Samuli, Karras, Tero, Aila, Timo, Herva, Antti, Saito, Shunsuke, Yu, Ronald, Li, Hao and Lehtinen, Jaakko), In Proceedings of the ACM SIGGRAPH / Eurographics Symposium on Computer Animation - SCA ’17, ACM Press, 2017.

Bibtex Entry:

@article{Laine_2017,
	title={Production-level facial performance capture using deep convolutional neural networks},
	ISBN={9781450350914},
	url={http://dx.doi.org/10.1145/3099564.3099581},
	DOI={10.1145/3099564.3099581},
	journal={Proceedings of the ACM SIGGRAPH / Eurographics Symposium on Computer Animation  - SCA  ’17},
	publisher={ACM Press},
	author={Laine, Samuli and Karras, Tero and Aila, Timo and Herva, Antti and Saito, Shunsuke and Yu, Ronald and Li, Hao and Lehtinen, Jaakko},
	year={2017},
	abstract = {We present a real-time deep learning framework for video-based facial performance capture -- the dense 3D tracking of an actor's face given a monocular video.}
	
}