  We introduce PICA, a novel representation for high-fidelity animatable
clothed human avatars with physics-accurate dynamics, even for loose clothing.
Previous neural rendering-based representations of animatable clothed humans
typically employ a single model to represent both the clothing and the
underlying body. While efficient, these approaches often fail to accurately
represent complex garment dynamics, leading to incorrect deformations and
noticeable rendering artifacts, especially for sliding or loose garments.
Furthermore, previous works represent garment dynamics as pose-dependent
deformations and facilitate novel pose animations in a data-driven manner. This
often results in outcomes that do not faithfully represent the mechanics of
motion and are prone to generating artifacts in out-of-distribution poses. To
address these issues, we adopt two individual 3D Gaussian Splatting (3DGS)
models with different deformation characteristics, modeling the human body and
clothing separately. This distinction allows for better handling of their
respective motion characteristics. With this representation, we integrate a
graph neural network (GNN)-based clothed body physics simulation module to
ensure an accurate representation of clothing dynamics. Our method, through its
carefully designed features, achieves high-fidelity rendering of clothed human
bodies in complex and novel driving poses, significantly outperforming previous
methods under the same settings.
