Differentiable {XPBD} for Gradient-Based Learning of Physical Parameters from Motion

Accurate cloth simulation is a vital component in computer graphics, virtual reality, and fashion design. Position-Based Dynamics ({PBD}) and its extension ({XPBD}) offer robust and efficient methods for simulating deformable objects like cloth. This paper details the evaluation and comparison of cloth simulations based on {XPBD}, including its ”small steps” variant and an Energy- Aware ({EA}) modification. The {XPBD} variants are evaluated for their physical plausibility and energy conservation to analyze their suitability for inverse problems. Furthermore, we explore the implementation of a differentiable {XPBD} simulator, enabling the estimation of material properties and external forces. The differentiable simulator is assessed for its capability to estimate parameters in scenarios of increasing complexity. Results indicate that small time steps with single iterations in {XPBD} offer good energy behavior, while the {EA} modification exhibits undesired characteristics. The differentiable simulator successfully estimates single parameters but identifies challenges with multi-parameter optimization due to compensatory effects.