  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.
