Fast and light-weight methods for animating 3D characters are desirable in
various applications such as computer games. We present a learning-based
approach to enhance skinning-based animations of 3D characters with vivid
secondary motion effects. We design a neural network that encodes each local
patch of a character simulation mesh where the edges implicitly encode the
internal forces between the neighboring vertices. The network emulates the
ordinary differential equations of the character dynamics, predicting new
vertex positions from the current accelerations, velocities and positions.
Being a local method, our network is independent of the mesh topology and
generalizes to arbitrarily shaped 3D character meshes at test time. We further
represent per-vertex constraints and material properties such as stiffness,
enabling us to easily adjust the dynamics in different parts of the mesh. We
evaluate our method on various character meshes and complex motion sequences.
Our method can be over 30 times more efficient than ground-truth physically
based simulation, and outperforms alternative solutions that provide fast
approximations.

本文提出了一种基于学习的方法，用于增强基于绑定的 3D 角色动画的生动的次要动作效果，通过设计用于编码角色动力学的神经网络，该网络可以预测出新的顶点位置。我们的方法可以高效且精确地模拟角色动态，并且在各种 3D 角色网格和复杂动作序列上得到证明。