Active soft bodies can affect their shape through an internal actuation
mechanism that induces a deformation. Similar to recent work, this paper
utilizes a differentiable, quasi-static, and physics-based simulation layer to
optimize for actuation signals parameterized by neural networks. Our key
contribution is a general and implicit formulation to control active soft
bodies by defining a function that enables a continuous mapping from a spatial
point in the material space to the actuation value. This property allows us to
capture the signal's dominant frequencies, making the method discretization
agnostic and widely applicable. We extend our implicit model to mandible
kinematics for the particular case of facial animation and show that we can
reliably reproduce facial expressions captured with high-quality capture
systems. We apply the method to volumetric soft bodies, human poses, and facial
expressions, demonstrating artist-friendly properties, such as simple control
over the latent space and resolution invariance at test time.

通过使用一个可微分的、准静态的、基于物理学的仿真层，以神经网络参数化为特征，本论文利用活动软体对形态施加内部驱动机制。关键贡献是通过定义一个函数实现空间点在材料空间中到激励值的连续映射，从而提供了捕获信号主导频率的特性。扩展了隐式模型到下颚运动学，特别是面部动画的情况，并展示了能够可靠地复制用高质量捕捉系统记录的面部表情的能力。我们将该方法应用于体积软体、人体姿势和面部表情，证明了对艺术家友好的属性，如在测试时对潜在空间的简单控制和解析度不变性。