Abstract Modeling of the human face is a challenging yet important problem in computer graphics. Building accurate muscle models for physics-based simulation of the face is a problem that either requires a lot of manual effort or drastic over-parameterization of the muscles to achieve desirable results. In this work, we reduce the number of parameters required to build personalized muscle models by taking into account the blending of the fine muscles and passive tissue when we solve for the muscle activations. We begin by adapting an anatomical template model to a neutral scan of a subject. Then, we solve an inverse physics problem using several scans simultaneously to solve for both the muscle activations and the geometry matrix representing blending of the muscles. Finally, we demonstrate that this geometry matrix can be used on new, previously unseen scans to solve for only the muscle activations. This greatly reduces the number of parameters that must be solved for compared to previous works while requiring no additional manual effort in constructing the muscles.

中文翻译：

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使用数据求解肌肉混合

摘要 人脸建模是计算机图形学中一个具有挑战性但又很重要的问题。为基于物理的面部仿真构建准确的肌肉模型是一个需要大量手动操作或肌肉过度参数化才能获得理想结果的问题。在这项工作中，我们通过在求解肌肉激活时考虑精细肌肉和被动组织的混合来减少构建个性化肌肉模型所需的参数数量。我们首先使解剖模板模型适应对象的中性扫描。然后，我们同时使用多次扫描来解决一个逆物理问题，以解决肌肉激活和代表肌肉混合的几何矩阵。最后，我们证明了这个几何矩阵可以用于新的、以前看不见的扫描只解决肌肉激活。与以前的工作相比，这大大减少了必须解决的参数数量，同时在构建肌肉时不需要额外的手动工作。