Chai, M, Shao, T, Wu, H et al. (2 more authors) (2016) AutoHair: Fully Automatic Hair Modeling from A Single Image. ACM Transactions on Graphics, 35 (4). ARTN 116. ISSN 0730-0301
Abstract
We introduce AutoHair, the first fully automatic method for 3D hair modeling from a single portrait image, with no user interaction or parameter tuning. Our method efficiently generates complete and high-quality hair geometries, which are comparable to those generated by the state-of-the-art methods, where user interaction is required. The core components of our method are: a novel hierarchical deep neural network for automatic hair segmentation and hair growth direction estimation, trained over an annotated hair image database; and an efficient and automatic data-driven hair matching and modeling algorithm, based on a large set of 3D hair exemplars. We demonstrate the efficacy and robustness of our method on Internet photos, resulting in a database of around 50K 3D hair models and a corresponding hairstyle space that covers a wide variety of real-world hairstyles. We also show novel applications enabled by our method, including 3D hairstyle space navigation and hair-aware image retrieval.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Graphics, VOL 35, ISS 4, July 2016. http://doi.acm.org/10.1145/2897824.2925961. |
Keywords: | hair modeling; image segmentation; data-driven modeling; deep neural network |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Aug 2018 10:25 |
Last Modified: | 07 Aug 2018 10:33 |
Status: | Published |
Publisher: | Association for Computing Machinery |
Identification Number: | 10.1145/2897824.2925961 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:134268 |