Automated biomechanical testing has great potential for the development of VR applications, as initial insights into user behaviour can be gained in silico early in the design process. In particular, it allows prediction of user movements and ergonomic variables, such as fatigue, prior to conducting user studies. However, there is a fundamental disconnect between simulators hosting state-of-the-art biomechanical user models and simulators used to develop and run VR applications. Existing user simulators often struggle to capture the intricacies and nuances of real-world VR applications, reducing ecological validity of user predictions. In this paper, we introduce SIM2VR, a system that aligns user simulation with a given VR application by establishing a continuous closed loop between the two processes. This, for the first time, enables training simulated users directly in the same VR application that real users interact with. We demonstrate that SIM2VR can predict differences in user performance, ergonomics and strategies in a fast-paced, dynamic arcade game. In order to expand the scope of automated biomechanical testing beyond simple visuomotor tasks, advances in cognitive models and reward function design will be needed.

翻译：自动化生物力学测试在虚拟现实应用开发中具有巨大潜力，可在设计早期阶段通过计算机模拟获得用户行为初步洞察。具体而言，它能在开展用户研究前预测用户运动方式及疲劳等人体工学变量。然而，现有采用先进生物力学用户模型的模拟器与用于开发及运行VR应用的模拟器之间存在根本性脱节。现有用户模拟器往往难以捕捉真实VR应用的复杂性与微妙差异，降低了用户预测的生态效度。本文提出SIM2VR系统，通过建立两个过程间的连续闭环回路，将用户模拟与特定VR应用对齐。该系统首次实现了在真实用户交互的VR应用中直接训练模拟用户。实验证明，SIM2VR能够预测快节奏动态街机游戏中用户表现、人体工学特性及策略的差异。为将自动化生物力学测试拓展至简单视动任务之外，未来需在认知模型与奖励函数设计方面取得进展。