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.

本文提出了 SoftSMPL，一种基于学习的方法，用于以身体形状和动作作为函数来建模逼真的软组织动力学，具有比现有技术更好的通用性，运用了新颖的运动描述符、神经网络的基于循环的回归器和高效的非线性变形子空间。