  Skin dynamics contributes to the enriched realism of human body models in
rendered scenes. Traditional methods rely on physics-based simulations to
accurately reproduce the dynamic behavior of soft tissues. Due to the model
complexity and thus the heavy computation, however, they do not directly offer
practical solutions to domains where real-time performance is desirable. The
quality shapes obtained by physics-based simulations are not fully exploited by
example-based or more recent datadriven methods neither, with most of them
having focused on the modeling of static skin shapes by leveraging quality
data. To address these limitations, we present a learningbased method for
dynamic skin deformation. At the core of our work is a recurrent neural network
that learns to predict the nonlinear, dynamics-dependent shape change over time
from pre-existing mesh deformation sequence data. Our network also learns to
predict the variation of skin dynamics across different individuals with
varying body shapes. After training the network delivers realistic,
high-quality skin dynamics that is specific to a person in a real-time course.
We obtain results that significantly saves the computational time, while
maintaining comparable prediction quality compared to state-of-the-art results.
