# Combining 3D Morphable Models: A Large scale Face-and-Head Model

## TL;DR

This paper introduces methods to combine multiple 3D Morphable Models with different templates and datasets, creating a comprehensive face-and-head model that outperforms existing models in accuracy and detail.

## Contribution

It proposes two novel techniques for merging 3DMMs and demonstrates a large-scale combined model that enhances head shape representation and reconstruction capabilities.

## Key findings

- The combined model achieves state-of-the-art performance.
- It outperforms existing head models significantly.
- The approach enables detailed head reconstruction from single images.

## Abstract

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.