Personalised 3D face creation has always been a hot topic in the computer vision community. Many methods have been proposed including the statistic model, the non-rigid registration and high-end depth acquisition equipment. However, in practical applications, those existing methods still have their own limitations. For example, the performance of the statistic model-based methods highly depends on the generality of the pre-trained statistic model; the non-rigid registration based methods are sensitive to the quality of input data; the high-end equipment-based methods are less able to be popularised due to the expensive equipment costs; the deep learning-based methods can only perform well if proper training data provided for the target domain, and require GPU for better performance. To this end, this paper presents an adaptive template augmented method that can automatically obtain a personalised 4D facial modelling only using a consumer-grade device. The noisy data from such a cheap device are well handled. The whole process consists of a series of linear solutions and can be achieved in real-time for online processing only based on the CPU computation on a laptop. There is no constraint nor complex operation required by the proposed method. No additional time-consumptive pre- or post-processing for the personalisation is needed. Comparisons against several existing methods demonstrate the superiority of the proposed method.

个性化的3D人脸创建一直是计算机视觉界的热门话题。已经提出了许多方法，包括统计模型，非刚性配准和高端深度采集设备。但是，在实际应用中，这些现有方法仍然有其自身的局限性。例如，基于统计模型的方法的性能在很大程度上取决于预先训练的统计模型的一般性。基于非刚性注册的方法对输入数据的质量敏感；基于高端设备的方法由于昂贵的设备成本而无法普及。基于深度学习的方法只有在为目标域提供了适当的训练数据的情况下才能表现良好，并且需要GPU才能获得更好的性能。为此，本文提出了一种自适应模板增强方法，该方法仅使用消费级设备即可自动获得个性化4D面部建模。这样便宜的设备产生的噪声数据得到了很好的处理。整个过程由一系列线性解决方案组成，并且仅基于笔记本电脑上的CPU计算就可以实时实现在线处理。所提出的方法没有约束，也不需要复杂的操作。不需要其他耗时的个性化预处理或后处理。与几种现有方法的比较证明了该方法的优越性。整个过程由一系列线性解决方案组成，并且仅基于笔记本电脑上的CPU计算就可以实时实现在线处理。所提出的方法没有约束，也不需要复杂的操作。不需要其他耗时的个性化预处理或后处理。与几种现有方法的比较证明了该方法的优越性。整个过程由一系列线性解决方案组成，并且仅基于笔记本电脑上的CPU计算就可以实时实现在线处理。所提出的方法没有约束，也不需要复杂的操作。不需要其他耗时的个性化预处理或后处理。与几种现有方法的比较证明了该方法的优越性。