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.

翻译：主动软体能够通过内部驱动机制引发变形，从而改变自身形状。与近期研究类似，本文利用可微分的准静态物理仿真层来优化由神经网络参数化的驱动信号。我们的核心贡献在于提出一种通用的隐式公式来控制主动软体：通过定义一个函数，实现从材料空间中的空间点到驱动值的连续映射。这一特性使我们能够捕捉信号的主导频率，从而使方法具备离散化无关性并广泛应用于多种场景。针对面部动画的特殊情况，我们将隐式模型扩展至下颌运动学，并证明该方法能够可靠地复现由高质量捕捉系统获取的面部表情。我们将该方法应用于体积软体、人体姿态及面部表情，展示了隐空间简单控制与测试时分辨率不变性等艺术友好特性。