We present the first motion generation system for playtesting virtual reality (VR) games. Our player model generates VR headset and handheld controller movements from in-game object arrangements, guided by style exemplars and aligned to maximize simulated gameplay score. We train on the large BOXRR-23 dataset and apply our framework on the popular VR game Beat Saber. The resulting model Robo-Saber produces skilled gameplay and captures diverse player behaviors, mirroring the skill levels and movement patterns specified by input style exemplars. Robo-Saber demonstrates promise in synthesizing rich gameplay data for predictive applications and enabling a physics-based whole-body VR playtesting agent.

翻译：我们提出了首个用于虚拟现实游戏试玩的运动生成系统。我们的玩家模型根据游戏内物体布局，在风格示例的指导下生成VR头显和手持控制器的运动，并通过调整以最大化模拟游戏得分。我们在大型数据集BOXRR-23上进行训练，并将框架应用于热门VR游戏《Beat Saber》。所得模型Robo-Saber能够生成高水平的游戏操作，并捕捉多样化的玩家行为，精确复现输入风格示例所指定的技能水平与运动模式。Robo-Saber在合成丰富游戏数据以用于预测性应用，以及实现基于物理学的全身VR试玩智能体方面展现出巨大潜力。