Implicit Neural Representation for Physics-driven Actuated Soft Bodies
Lingchen Yang Byungsoo Kim Gaspard Zoss Baran Gözcü Markus Gross Barbara Solenthaler
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.