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.
翻译:在诸如计算机游戏等各种应用中,动画 3D 字符的快速和轻量法是可取的。 我们展示了一种基于学习的办法来增强3D 字符的光化动画,并具有生动的二次运动效果。 我们设计了一个神经网络, 将字符模拟网状的每个本地部分编码成一个字符模拟网格, 边际可以隐含地将相邻的脊椎之间的内部力量编码起来。 网络模仿字符动态的普通差异方程式, 从当前加速度、 速度和位置中预测新的顶点位置。 作为本地方法, 我们的网络独立于网状表层, 并且一般化为测试时任意形状的 3D 字符 meshes 。 我们进一步代表每个垂直的制约和物质特性特性, 如坚硬度, 使我们能够轻松地调整网状中不同部分的动态。 我们评估我们的方法可以比基于地面的模拟效率高30倍以上, 并且超越提供快速近似的替代方法。