Robo-Saber: Generating and Simulating Virtual Reality Players
This work addresses the need for automated playtesting in VR games, which is an incremental advancement in motion generation for specific applications.
The paper tackles the problem of generating realistic player motions for virtual reality (VR) games to enable automated playtesting, resulting in a model called Robo-Saber that produces skilled gameplay and captures diverse player behaviors as demonstrated on the VR game Beat Saber.
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.