Learning 3D face reconstruction from a single sketch

Yang, Li, Wu, Jing ORCID: https://orcid.org/0000-0001-5123-9861, Huo, Jing, Lai, Yu-Kun ORCID: https://orcid.org/0000-0002-2094-5680 and Gao, Yang 2021. Learning 3D face reconstruction from a single sketch. Graphical Models 115 , 101102. 10.1016/j.gmod.2021.101102

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Abstract

3D face reconstruction from a single image is a classic computer vision problem with many applications. However, most works achieve reconstruction from face photos, and little attention has been paid to reconstruction from other portrait forms. In this paper, we propose a learning-based approach to reconstruct a 3D face from a single face sketch. To overcome the problem of no paired sketch-3D data for supervised learning, we introduce a photo-to-sketch synthesis technique to obtain paired training data, and propose a dual-path architecture to achieve synergistic 3D reconstruction from both sketches and photos. We further propose a novel line loss function to refine the reconstruction with characteristic details depicted by lines in sketches well preserved. Our method outperforms the state-of-the-art 3D face reconstruction approaches in terms of reconstruction from face sketches. We also demonstrate the use of our method for easy editing of details on 3D face models.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Elsevier
ISSN:	1524-0703
Date of First Compliant Deposit:	12 April 2021
Date of Acceptance:	16 March 2021
Last Modified:	08 Nov 2023 06:40
URI:	https://orca.cardiff.ac.uk/id/eprint/140434

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