We introduce ViSA (Virtual Stunt Actors), an interactive animation system designed to create realistic ballistic stunt actions frequently seen in filmmaking and TV production. By providing spatial constraints suitable for the desired stunt scene, our system generates physically plausible motions satisfying the given constraints. The problem is formulated as a deep reinforcement learning task, incorporating a novel state and action spaces, as well as straightforward yet effective rewards for ballistic stunt actions. Users can receive a fast response within several minutes and continue to choreograph complex stunt scenes in an interactive manner. We demonstrate ballistic stunt scenes resembling those in various films and TV dramas, such as traffic accidents, falling down stairs, and falls from buildings. The effectiveness of the technical components and design choices in our system is demonstrated through extensive comparisons, analyses, and ablation studies.

Wearable robots have been developed to aid or substitute the gait locomotion of humans. To assist gait locomotion based on the intention of a wearer, a gait pattern analysis is required with a wearable sensor by measuring body information, i.e., a joint angular velocity. However, measuring a precise joint angular velocity is difficult because the attachment position of a sensor has a curvature and an anatomical joint axis which is invisible. Therefore, a sensor calibration algorithm, which aligns a sensor axis into an anatomical joint axis, is required to provide an optimal assist for a wearer. Hence, in this paper, a new and simple sensor calibration algorithm is proposed with a unit sensor. Since a wearer shakes the body or collides with the ground when walking, the attachment position of a sensor may be changed. Thus, a continuous sensor compensation algorithm is also proposed. Additionally, the effectiveness of this new algorithm is demonstrated by gait locomotion experiments on various paths.