Caricature is an artistic abstraction of the human face by distorting or exaggerating certain facial features, while still retains a likeness with the given face. Due to the large diversity of geometric and texture variations, automatic landmark detection and 3D face reconstruction for caricature is a challenging problem and has rarely been studied before. In this paper, we propose the first automatic method for this task by a novel 3D approach. To this end, we first build a dataset with various styles of 2D caricatures and their corresponding 3D shapes, and then build a parametric model on vertex based deformation space for 3D caricature face. Based on the constructed dataset and the nonlinear parametric model, we propose a neural network based method to regress the 3D face shape and orientation from the input 2D caricature image. Ablation studies and comparison with state-of-the-art methods demonstrate the effectiveness of our algorithm design. Extensive experimental results demonstrate that our method works well for various caricatures. Our constructed dataset, source code and trained model are available at https://github.com/Juyong/CaricatureFace.

讽刺漫画是通过扭曲或夸大某些面部特征对人脸的艺术抽象，同时仍保留与给定面孔的相似度。由于几何和纹理变化的多样性，自动地标检测和用于漫画的3D人脸重建是一个具有挑战性的问题，以前很少进行研究。在本文中，我们通过一种新颖的3D方法提出了用于此任务的第一种自动方法。为此，我们首先建立具有各种样式的2D漫画及其对应3D形状的数据集，然后在基于顶点的3D漫画脸部变形空间上建立参数模型。基于构造的数据集和非线性参数模型，我们提出了一种基于神经网络的方法，用于从输入的2D漫画图像中回归3D面部形状和方向。消融研究以及与最先进方法的比较证明了我们算法设计的有效性。大量的实验结果表明，我们的方法适用于各种漫画。我们构建的数据集，源代码和经过训练的模型可在https://github.com/Juyong/CaricatureFace中获得。