论文标题
面景观：大规模的高质量3D面部数据集和详细的可操作3D面孔预测
FaceScape: a Large-scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction
论文作者
论文摘要
在本文中，我们提出了一个大规模详细的3D脸部数据集，面对景观，并提出了一种新型算法，该算法能够从单个图像输入中预测精美的可操作的3D面部模型。 FaceScape数据集提供18,760个纹理的3D面，从938名受试者捕获，每个受试者都有20个特定表达式。 3D模型包含孔隙水平的面部几何形状，该几何形状也被处理为拓扑均匀。这些精细的3D面部模型可以表示为粗糙形状和位移图的3D形态模型，用于详细的几何形状。利用大规模和高精度数据集的优势，提出了一种新型算法，以使用深神经网络来学习表达特定的动态细节。从单个图像输入中，学到的关系是我们3D面孔预测系统的基础。与以前的方法不同，我们的预测3D模型在不同表达式下具有高度详细的几何形状可操作。前所未有的数据集和代码将出于研究目的向公众发布。
In this paper, we present a large-scale detailed 3D face dataset, FaceScape, and propose a novel algorithm that is able to predict elaborate riggable 3D face models from a single image input. FaceScape dataset provides 18,760 textured 3D faces, captured from 938 subjects and each with 20 specific expressions. The 3D models contain the pore-level facial geometry that is also processed to be topologically uniformed. These fine 3D facial models can be represented as a 3D morphable model for rough shapes and displacement maps for detailed geometry. Taking advantage of the large-scale and high-accuracy dataset, a novel algorithm is further proposed to learn the expression-specific dynamic details using a deep neural network. The learned relationship serves as the foundation of our 3D face prediction system from a single image input. Different than the previous methods, our predicted 3D models are riggable with highly detailed geometry under different expressions. The unprecedented dataset and code will be released to public for research purpose.
