Three-dimensional Morphable Models (3DMMs) are powerful statistical tools for representing the 3D surfaces of an object class. In this context, we identify an interesting question that has previously not received research attention: is it possible to combine two or more 3DMMs that (a) are built using different templates that perhaps only partly overlap, (b) have different representation capabilities and (c) are built from different datasets that may not be publicly-available? In answering this question, we make two contributions. First, we propose two methods for solving this problem: i. use a regressor to complete missing parts of one model using the other, ii. use the Gaussian Process framework to blend covariance matrices from multiple models. Second, as an example application of our approach, we build a new face-and-head shape model that combines the variability and facial detail of the LSFM with the full head modelling of the LYHM. The resulting combined shape model achieves state-of-the-art performance and outperforms existing head models by a large margin. Finally, as an application experiment, we reconstruct full head representations from single, unconstrained images by utilizing our proposed large-scale model in conjunction with the FaceWarehouse blendshapes for handling expressions.

本文提出了两种方法，一种是使用回归器来完成一个模型缺失部分的替换，另一种是使用高斯过程框架来融合多个模型的协方差矩阵，使用这些方法成功地构建了一个新的面部和头部形状模型，并通过与FaceWarehouse blendshapes结合使用该模型进行了图像重建实验，达到了最先进性能，并且在头部建模方面表现出了较大的优势。

结合3D形态模型: 一个大规模的人脸和头部模型