Boba: Batched Simulation for Physics-Based Gaussian Digital Twins

Digital twins replicate the appearance and physical behav-ior of real-world objects for interactive simulation in XR, robotics, andgaming. While neural representations such as 3D Gaussians enable high-fidelity twins reconstructed from images, existing systems simulate onlyone instance at a time and barely reach real-time performance, makinglarge-scale rollout evaluation for robot learning, model predictive control,and multi-user XR impractical. We present Boba, the first batched simula-tor for physics-based Gaussian digital twins. Our key idea is to separate thestatic twin template from the dynamic simulation state and co-design thephysics, deformation, and rendering/visualization pipelines for batchedexecution. This structure enables shared template computation acrossinstances while minimizing memory traffic and synchronization overhead.Guided by this principle, we introduce compact surrogate spring–massmodels and a set of system-level optimizations—including mixed-precisionGaussian skinning, memory-efficient execution, and shared-memory-awarebatching—that significantly reduce compute and memory costs. On anXR-configured Jetson Orin, Boba’s local configuration achieves over 10×single-instance speedup over the prior state-of-the-art PhysTwin pipeline.Its distributed configuration, split between the Orin and a server, reaches25.1 ms end-to-end latency, 13.2× lower than the same PhysTwin base-line on Orin, while lowering incremental power by 22.2% relative toBoba’s local configuration. For batched execution, Boba scales to 3,310FPS on RTX 4090, a 16.2× system-level aggregate-throughput improve-ment over Boba’s optimized single-instance configuration on the sameGPU, and accelerates predictive control for deformable object manipula-tion, achieving speedups from 26× to an estimated > 2,000× relative toPhysTwin-based planning. These results show that Boba can supportpower-constrained XR applications and scalable robot planning.