The 3D Morphable Model (3DMM), which is a Principal Component Analysis (PCA) based statistical model that represents a 3D face using linear basis functions, has shown promising results for reconstructing 3D faces from single-view in-the-wild images. However, 3DMM has restricted representation power due to the limited number of 3D scans and the global linear basis. To address the limitations of 3DMM, we propose a straightforward learning-based method that reconstructs a 3D face mesh through Free-Form Deformation (FFD) for the first time. FFD is a geometric modeling method that embeds a reference mesh within a parallelepiped grid and deforms the mesh by moving the sparse control points of the grid. As FFD is based on mathematically defined basis functions, it has no limitation in representation power. Thus, we can recover accurate 3D face meshes by estimating appropriate deviation of control points as deformation parameters. Although both 3DMM and FFD are parametric models, it is difficult to predict the effect of the 3DMM parameters on the face shape, while the deformation parameters of FFD are interpretable in terms of their effect on the final shape of the mesh. This practical advantage of FFD allows the resulting mesh and control points to serve as a good starting point for 3D face modeling, in that ordinary users can fine-tune the mesh by using widely available 3D software tools. Experiments on multiple datasets demonstrate how our method successfully estimates the 3D face geometry and facial expressions from 2D face images, achieving comparable performance to the state-of-the-art methods.
翻译:3D 软体模型(3DMM) 3D 软体模型(3DMM) 是一种基于本部组件分析(PCA) 的统计模型,它代表了使用线性基本功能的3D脸部,它显示了从单视视网形图像中重建3D脸部的有希望的结果。然而,3DMM由于3D扫描数量有限和全球线性基础,其显示能力受到限制。为了解决3DMM的局限性,我们提出了一种直接的基于学习的方法,首次通过自由格式变形(FFD)重建3D脸部的网形。FDD是一种几何模型方法,它把一个参考网格嵌入平行的网格中,并通过移动网状中分散的控制点来使3D的网形部分变形。由于FDFD以数学定义为基础,因此没有限制其表达能力。因此,我们可以通过估计控制点的适当偏差来恢复准确的3D面部图像。虽然 3DD是分数模型,但很难预测3D 的面面面面图表面图表面的表达效果的效果,而使3D 3D 变形的精确的参数能够解释成为FDFD 。