论文标题
SOFTSMPL：参数人类非线性软组织动力学的数据驱动建模
SoftSMPL: Data-driven Modeling of Nonlinear Soft-tissue Dynamics for Parametric Humans
论文作者
论文摘要
我们提出了SoftSMPL，这是一种基于学习的方法，可将现实的软组织动力学建模为身体形状和运动的函数。学习此类任务的数据集稀缺且生成昂贵，这使得训练模型容易过度拟合。在我们方法的核心方面，有三个关键贡献使我们能够比最先进的方法对高度逼真的动态和更好的概括能力建模，同时训练相同的数据。首先，一种新型的运动描述符，通过删除特定于主题的特征来消除标准姿势表示。其次，基于神经网络基于神经网络的复发器，它概括为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。我们证明了对现有方法的质量和定量改进，此外，我们在各种运动捕获数据库上展示了方法的鲁棒性。
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.
