SSILK : Self‐Supervised Integration of Latent Kinematics for Joint‐Driven Neural Garments

Abstract Garment animation systems are essential for high‐fidelity digital characters in various applications such as films and video games. We propose SSILK, a neural network‐based method that is explicitly designed to handle loose garments such as capes and robes, garment types that are notoriously hard to simulate via pose‐based garment neural simulators. We achieve this through three contributions. First, we formulate our method as a latent‐space time integration scheme inspired by Verlet integration, resulting in more temporally stable garment dynamics compared to prior methods. Second, we employ a hybrid proxy‐joint and blendshape decoder to represent full SO(3) transformations that can be too challenging to capture using blend‐shapes alone. Finally, we propose a data‐augmentation strategy to improve performance on out‐of‐distribution scenarios. We demonstrate the effectiveness of our method on various garment and character animations and compare it to the state of the art.