论文标题
使用非线性参数模型的地标检测和3D面部重建漫画的重建
Landmark Detection and 3D Face Reconstruction for Caricature using a Nonlinear Parametric Model
论文作者
论文摘要
漫画是通过扭曲或夸大某些面部特征而对人脸的艺术抽象，而仍然与给定的脸保持相似。由于几何和质地变化的多样性，自动具有里程碑意义的检测和漫画的3D面部重建是一个具有挑战性的问题，并且以前很少研究。在本文中，我们通过一种新颖的3D方法提出了针对此任务的第一个自动方法。为此，我们首先构建一个具有各种风格的2D漫画及其相应3D形状的数据集，然后在基于顶点的变形空间上为3D漫画面构建一个参数模型。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，以从输入2D漫画图像中回归3D面向形状和方向。消融研究和与最先进方法的比较证明了我们算法设计的有效性。广泛的实验结果表明，我们的方法适合各种漫画。我们构建的数据集，源代码和训练有素的模型可在https://github.com/juyong/caricatureface上找到。
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.
