We present SoftSMPL, a learning‐based method to model realistic soft‐tissue dynamics as a function of body shape and motion. Datasets to learn such task are scarce and expensive to generate, which makes training models prone to overfitting. At the core of our method there are three key contributions that enable us to model highly realistic dynamics and better generalization capabilities than state‐of‐the‐art methods, while training on the same data. First, a novel motion descriptor that disentangles the standard pose representation by removing subject‐specific features; second, a neural‐network‐based recurrent regressor that generalizes to unseen shapes and motions; and third, a highly efficient nonlinear deformation subspace capable of representing soft‐tissue deformations of arbitrary shapes. We demonstrate qualitative and quantitative improvements over existing methods and, additionally, we show the robustness of our method on a variety of motion capture databases.

中文翻译：

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SoftSMPL：参数化人类非线性软组织动力学的数据驱动建模

我们提出了 SoftSMPL，这是一种基于学习的方法，可将真实的软组织动力学建模为身体形状和运动的函数。用于学习此类任务的数据集稀缺且生成成本高昂，这使得训练模型容易过度拟合。我们方法的核心是三个关键贡献，它们使我们能够在对相同数据进行训练的同时，对高度逼真的动态和比最先进方法更好的泛化能力进行建模。首先，一种新颖的运动描述符，通过去除特定主题的特征来解开标准姿势表示；第二，一个基于神经网络的递归回归器，可以泛化到看不见的形状和运动；第三，一个高效的非线性变形子空间，能够表示任意形状的软组织变形。