Efficient Soft Body Simulation for Real-Time Applications: A GPU-Based XPBD Approach

Soft tissue simulation is a complex and challenging research field with applications across various domains, including engineering, design, medicine, and biomechanics. By accurately modeling soft tissues, virtual and augmented reality applications—such as medical training simulations, image-guided surgical support systems, and injury analysis—can more effectively replicate the behavior of complex human body structures. While many models have been proposed to simulate soft bodies, a major challenge has always been the heavy computational workload required to model and reproduce elastic and mechanical interactions between structures in the human body, usually not optimal for real-time applications. The aim of this work is to present an innovative workflow to simulate soft tissues in real time, leveraging the computational power offered by graphic processing units (GPUs). The proposed workflow addresses the challenge of improving the real-time performance of soft bodies deformation based on Extended Position Based Dynamics (XPBD) by proposing an original two-fold integrated approach: firstly, we optimized the XPBD model for parallel execution by using graph coloring. This technique allowed us to run the simulation without the use of atomic operations, while retaining the faster convergence speed of the parallel Gauss-Seidel solver. Subsequently, we applied it on a soft body represented as a volumetric regular lattice of particles, instead of the well-known representation based on tetrahedral or hexagonal meshes, allowing us to apply the deformation results even on highly polygonal meshes. The effectiveness of the developed system is evaluated through a series of tests aimed at proving its accuracy and performance, with a focus on providing realistic visual representation alongside computational efficiency.