: Top: Skeletal, facial, and hand motions are tracked by off-the-shelf sensors. From sparse user-defined examples, our method reliably separates and extrapolates simultaneously-performed gestures to control characters in games or VR. Bottom left: Dog happy walk, neutral run, shaking, and sitting. Bottom right: Caterpillar crawl variations, controlled by varying amplitude, phase, and frequency of input gestures. Motion-tracked real-time character control is important for games and VR, but current solutions are limited: retargeting is hard for nonhuman characters, with locomotion bound to the sensing volume; and pose mappings are ambiguous with difficult dynamic motion control. We robustly estimate wave properties -amplitude, frequency, and phase -for a set of interactively-defined gestures by mapping user motions to a low-dimensional independent representation. The mapping separates simultaneous or intersecting gestures, and extrapolates gesture variations from single training examples. For animations such as locomotion, wave properties map naturally to stride length, step frequency, and progression, and allow smooth transitions from standing, to walking, to running. Interpolating outof-phase locomotions is hard, e.g., quadruped legs between walks and runs switch phase, so we introduce a new time-interpolation scheme to reduce artifacts. These improvements to real-time motiontracked character control are important for common cyclic animations. We validate this in a user study, and show versatility to apply to part-and full-body motions across a variety of sensors.
The authors of REF introduced one approach to address the problem that retargeting is hard for nonhuman characters, with locomotion bound to the sensing volume; and pose mappings are ambiguous with difficult dynamic motion control.
Generalizing wave gestures from sparse examples for real-time character control
TOGS
Computer Science