Data‐driven models of human avatars have shown very accurate representations of static poses with soft‐tissue deformations. However they are not yet capable of precisely representing very nonlinear deformations and highly dynamic effects. Nonlinear skin mechanics are essential for a realistic depiction of animated avatars interacting with the environment, but controlling physics‐only solutions often results in a very complex parameterization task. In this work, we propose a hybrid model in which the soft‐tissue deformation of animated avatars is built as a combination of a data‐driven statistical model, which kinematically drives the animation, an FEM mechanical simulation. Our key contribution is the definition of deformation mechanics in a reference pose space by inverse skinning of the statistical model. This way, we retain as much as possible of the accurate static data‐driven deformation and use a custom anisotropic nonlinear material to accurately represent skin dynamics. Model parameters including the heterogeneous distribution of skin thickness and material properties are automatically optimized from 4D captures of humans showing soft‐tissue deformations.

中文翻译：

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动画化身的非线性皮肤力学建模和估计

人类化身的数据驱动模型已经显示出具有软组织变形的静态姿势的非常准确的表示。然而，它们还不能精确地表示非常非线性的变形和高度动态的效果。非线性皮肤力学对于真实描绘与环境交互的动画化身至关重要，但控制纯物理解决方案通常会导致非常复杂的参数化任务。在这项工作中，我们提出了一种混合模型，其中动画化身的软组织变形被构建为数据驱动统计模型的组合，该模型在运动学上驱动动画，FEM 机械模拟。我们的主要贡献是通过统计模型的反向蒙皮定义参考姿势空间中的变形力学。这条路，我们尽可能多地保留了精确的静态数据驱动变形，并使用自定义的各向异性非线性材料来准确表示皮肤动力学。模型参数包括皮肤厚度和材料特性的异质分布，是从显示软组织变形的人体 4D 捕获中自动优化的。