首页|Learning 3D face reconstruction from a single sketch
Learning 3D face reconstruction from a single sketch
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NETL
NSTL
Elsevier
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.
3D face reconstructionSketchCharacteristic details
Li Yang、Jing Wu、Jing Huo、Yu-Kun Lai、Yang Gao
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State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
School of Computer Science & Informatics, Cardiff University, Cardiff, Wales, UK
NETL
NSTL
Elsevier
