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.

从单个图像重建3D人脸是许多应用中的经典计算机视觉问题。但是，大多数作品都是通过面部照片实现重建的，很少关注其他肖像形式的重建。在本文中，我们提出了一种基于学习的方法，可以从单脸素描中重建3D脸。为了克服在监督学习中没有成对的草图3D数据的问题，我们引入了一种“照片到草图”合成技术来获取成对的训练数据，并提出了一种双路径架构，以从草图和照片中实现协同的3D重建。我们进一步提出了一种新颖的线损函数，以利用保留得很好的草图中的线所描绘的特征细节来完善重构。从人脸草图的重建方面，我们的方法优于最新的3D人脸重建方法。我们还将演示如何使用我们的方法轻松编辑3D面部模型上的细节。