Extreme Reality (EXR) is to superimpose extreme situations and live sensory data to the physical scene. In this study, we explore gesture capture and interaction technology for emergency response training, which includes extreme gaits such as crawling through the floor and real-time gesture control of the user’s movement in the training scenarios. Existing motion tracking systems heavily rely on wearable inertial motion units (IMUs) or head-mounted displays (HMDs), which adds both cost and physical burden to the user. This study examines a new multimodal Extreme Reality interface to support the real-time operation and training without the need for wearable equipment. In this study, we measured gesture detection latency, key point requirements, and accuracy with LiDAR and webcam for complex gesture recognition. Our experiments show that the usability of the Extreme Reality interfaces can be tested in a simulated environment and measured with instruments objectively.